Digital twin calibration

Abstract

The invention relates to digital twin calibration. Systems or techniques are provided for facilitating improved digital twin calibration for scientific instruments. In various embodiments, the system can synchronize a parametric state of the digital twinning with a physical state of the scientific instrument via execution of a full-state Bayesian filter. In various instances, the full-state Bayesian filter can include a set of calibration iterations, each calibration iteration in the set of calibration iterations can include Bayesian updates to all of the parametric states based on iterations that can be presented by the scientific instrument and that can be simulated by the digital twinning that share observable amounts. In various instances, the scientific instrument can be a charged particle microscope, the parametric state of the digital twin can be an aberration coefficient vector of the charged particle microscope, and the iterative common observable quantity can be a Fourier transform of a converged beam electron diffraction pattern of an amorphous carbon sample captured by the charged particle microscope.

Description

Background Art

[0001] Scientific instruments can include complex arrangements of actuatable components, sensors, or consumables. Digital twins can simulate or predict the behavior of scientific instruments. In order for such simulation or prediction to be accurate, the digital twins should first be calibrated or synchronized with their corresponding scientific instruments. Summary of the Invention

[0002] The following presents a summary of the invention to provide a basic understanding of one or more embodiments. This summary of the invention is not intended to identify key or important elements, or to delineate any scope of a particular embodiment or any scope of the claims. Its sole purpose is to present concepts in a simplified form as a prelude to the more detailed description that is presented later. In one or more embodiments described herein, devices, systems, computer-implemented methods, apparatuses, or computer program products that facilitate improved calibration of digital twins for scientific instruments are described.

[0003] According to one or more embodiments, a scientific instrument is provided. The scientific instrument can include a non-transitory computer-readable memory that can store computer-executable components. The scientific instrument can further include a processor that can be operatively coupled to the non-transitory computer-readable memory and can execute the computer-executable components stored in the non-transitory computer-readable memory. In various embodiments, the computer-executable components can include an access component that can access a digital twin of the scientific instrument. In various aspects, the computer-executable components can include a calibration component that can synchronize the parameter state of the digital twin with the physical state of the scientific instrument via execution of a full-state Bayesian filter. In various cases, the full-state Bayesian filter can include a set of calibration iterations, and each calibration iteration in the set of calibration iterations can include a Bayesian update of all the parameter states based on iteratively shared observables that can be exhibited by the scientific instrument and simulated by the digital twin.

[0004] According to one or more embodiments, a computer-implemented method is provided. In various embodiments, the computer-implemented method can include synchronizing the parameter state of a digital twin with the physical state of a scientific instrument by a device operatively coupled to a processor that executes a full-state Bayesian filter. In various aspects, the computer-implemented method can include generating, by the device and in response to the synchronization, an electronic alert indicating that the digital twin is ready to predict the behavior of the scientific instrument. In various cases, the full-state Bayesian filter can include a set of calibration iterations, and each calibration iteration in the set of calibration iterations can include a Bayesian update of all the parameter states based on iteratively shared observables that can be exhibited by the scientific instrument and simulated by the digital twin.

[0005] According to one or more embodiments, a computer program product for facilitating improved digital twin calibration for a scientific instrument is provided. In various embodiments, the computer program product may include a non-transitory computer-readable memory containing program instructions. In various aspects, the program instructions may be executable by a processor to cause the processor to access a digital twin of a charged particle microscope. In various cases, the program instructions may be further executable to cause the processor to synchronize a parameter state of the digital twin with a physical state of the charged particle microscope via execution of a set of calibration iterations, each calibration iteration of the set of calibration iterations may include a Bayesian update of all the parameter states based on iteratively shared observables that can be exhibited by the charged particle microscope and simulated by the digital twin. In various cases, the program instructions may be further executable to cause the processor to forecast how the charged particle microscope will respond to a proposed usage scenario by running the digital twin after synchronization. BRIEF DESCRIPTION OF THE DRAWINGS

[0006] Various embodiments will be readily understood by the following detailed description in conjunction with the accompanying drawings. For the sake of description, like reference numerals designate like structural elements. The embodiments are illustrated by way of example and not by way of limitation in the figures. These figures are not necessarily drawn to scale.

[0007] Figure 1 An example non-limiting block diagram of a scientific instrument module in accordance with various embodiments described herein is illustrated.

[0008] Figure 2 An example non-limiting flowchart of a computer-implemented method in accordance with various embodiments described herein is illustrated.

[0009] Figure 3 A block diagram of an example non-limiting scientific instrument for facilitating improved digital twin calibration in accordance with one or more embodiments described herein is illustrated.

[0010] Figure 4 An example non-limiting block diagram is illustrated showing a set of controllable instrument settings, a parameter state, a set of input variables, and a set of output variables in accordance with one or more embodiments described herein.

[0011] Figure 5 A block diagram of an example non-limiting scientific instrument including a full-state Bayesian filter and calibration parameter state instantiation for facilitating improved digital twin calibration in accordance with one or more embodiments described herein is illustrated.

[0012] Figure 6 An example non-limiting block diagram of a full-state Bayesian filter in accordance with one or more embodiments described herein is illustrated.

[0013] Figures 7 to 12 Illustrates an example non - restrictive block diagram showing iterations of a full - state Bayesian filter in accordance with one or more embodiments described herein.

[0014] Figures 13 to 15 Illustrates a flowchart of an example non - restrictive computer - implemented method for facilitating improved digital twin calibration in accordance with one or more embodiments described herein.

[0015] Figures 16 to 22 Illustrates an example non - restrictive diagram of experimental results in accordance with one or more embodiments described herein.

[0016] Figure 23 Illustrates an example non - restrictive block diagram of a graphical user interface that can be used to perform some or all of the methods or techniques disclosed herein in accordance with various embodiments described herein.

[0017] Figure 24 Illustrates an example non - restrictive block diagram of a computing device that can execute some or all of the methods or techniques disclosed herein in accordance with various embodiments described herein.

[0018] Figure 25 Illustrates an example non - restrictive block diagram of a scientific instrument support system in which some or all of the methods or techniques disclosed herein can be executed in accordance with various embodiments described herein.

[0019] Figure 26 Illustrates a block diagram of an example non - restrictive operating environment in which one or more embodiments described herein can be facilitated.

[0020] Figure 27 Illustrates an example networked environment operable to perform various implementations described herein. Detailed Description

[0021] The following detailed description is merely illustrative and is not intended to limit the embodiments and / or the application or use of the embodiments. Further, there is no intention to be bound by any information presented in the preceding background or summary of the invention or detailed description sections.

[0022] One or more embodiments are now described with reference to the accompanying drawings, where like reference numerals are always used to refer to like elements. In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a more thorough understanding of one or more embodiments. However, it is apparent that in various instances, one or more embodiments may be practiced without these specific details.

[0023] The various operations may be described serially in a manner that is most helpful in understanding the subject matter disclosed herein as a number of discrete actions or operations. However, the order of description should not be construed as implying that these operations are necessarily order dependent. In particular, these operations may be performed in an order different from the order of presentation. The described operations may be performed in an order different from the described embodiments. Various additional operations may be performed, or the described operations may be omitted in additional embodiments.

[0024] Although some elements may be expressed in the singular (e.g., “processing device”), any suitable element may be represented by multiple instances of that element, and vice versa. For example, a set of operations described as being performed by a processing device may be implemented by different processing devices performing different ones of those operations. As used herein, the phrase “based on” should be understood to mean “at least partially based on” unless otherwise specified.

[0025] A scientific instrument (e.g., a mass spectrometer, an electron microscope) may be any suitable computerized device that can capture electronic measurements in the context of scientific, laboratory, research, or clinical operations. A scientific instrument may include a complex arrangement of actuatable components (e.g., ion sources, ion lenses, heaters, coolers, fluid valves, fluid pumps, circuit switches, sample stages, apertures), sensors (e.g., ion detectors, voltmeters, thermistors, potentiometers, pressure gauges), or consumables (e.g., carrier fluids, calibration agents, filters).

[0026] A digital twin of a scientific instrument may be any suitable collection of mathematical or physics-based models (e.g., mass continuity equations, energy conservation equations) that together can simulate or predict the future behavior of the scientific instrument (e.g., these models can calculate how a complex arrangement of the actuatable components, sensors, or consumables of the scientific instrument, or any part thereof, will be affected by any given planned or proposed use of the scientific instrument). In order for the digital twin to accurately or correctly facilitate such simulation or prediction, the digital twin should first be calibrated or otherwise synchronized with the scientific instrument. In other words, any parameters that make up or define the digital twin should be assigned values that are in close agreement with any physical characteristics of the scientific instrument that those parameters represent. If the values of the parameters assigned to the digital twin are not in close agreement with the physical characteristics of the scientific instrument, the simulation or prediction calculated by the digital twin may not accurately reflect the behavior of the scientific instrument.

[0027] Various parameters typically represent or quantify physical characteristics of a scientific instrument that cannot be directly controlled or selected. For example, a damping coefficient may represent a type of physical characteristic of a scientific instrument that affects how the scientific instrument responds kinetically or kinematically to different inputs, but the scientific instrument typically does not have any buttons, knobs, joysticks, or other interface devices that allow direct selection of the damping coefficient. Thus, calibration of such parameters may involve performing experiments or tests on the scientific instrument in order to estimate or infer what numerical values these parameters should be assigned.

[0028] As recognized by the inventors of the various embodiments described herein, the prior art promotes calibration of digital twin parameters in a piecemeal and non-standardized manner. In particular, when given a digital twin with a total set of parameters, the prior art decomposes the total set of parameters into disjoint subsets, identifies which of these subsets depend on which other subsets among these subsets, and calibrates these subsets in order of dependence by using subset-specific test procedures.

[0029] For example, consider a digital twin whose total set of parameters is: the length, width, and height of the instrument; the stiffness and yield strength of the instrument; and the damping coefficient of the instrument. In this case, the prior art would involve determining that: once the geometry is known, the stiffness and yield strength can be estimated experimentally; and once the stiffness, yield strength, and geometry are known, the damping coefficient can be estimated experimentally. Thus, such prior art would first measure the length, width, and height of the scientific instrument and assign these measurements to the length, width, and height parameters of the digital twin. Then, such prior art would estimate the stiffness and yield strength of the scientific instrument by performing one or more load-displacement tests on the scientific instrument (e.g., the data collected from these load-displacement tests can be processed statistically in combination with the measured geometry to calculate the stiffness and yield strength), and then, these estimates would be assigned to the stiffness and yield strength parameters of the digital twin. Finally, such prior art would estimate the damping coefficient of the scientific instrument by performing one or more vibration tests or initial condition tests on the scientific instrument (e.g., the data collected from these vibration or initial condition tests can be processed statistically in combination with the measured geometry and the estimated stiffness and yield strength to calculate the damping coefficient), and then, these estimates would be assigned to the damping coefficient parameter of the digital twin. Note that such prior art performs calibration in the order of parameter dependencies (e.g., first calibrating the geometry parameters; the stiffness and yield strength parameters depend on the geometry parameters and thus are only calibrated after the geometry parameters are calibrated; the damping coefficient parameter depends on the geometry parameters as well as the stiffness and yield strength parameters and thus is only calibrated after the geometry parameters and the stiffness and yield strength parameters are calibrated). Additionally, note that such prior art uses different or unique tests, metrics, or observables for each identified subset of parameters (e.g., directly measuring the geometry parameters; estimating the stiffness and yield strength parameters via load-displacement metrics; estimating the damping coefficient parameter via vibration metrics).

[0030] Unfortunately, such prior art has various drawbacks.

[0031] First, as recognized by the present inventor, such prior art cannot be generalized beyond simple, well-behaved parameters. In fact, such prior art can only be used for digital twin parameters that are not cumbersome, not intricately intertwined, or have easily or clearly understood interrelationships. Simple parameters that are physically easy to visualize (such as geometric shapes, stiffness, yield strength, or damping coefficients) meet these conditions. But more cumbersome parameters that are not physically easy to visualize (such as optical aberration coefficients; or elements or parts of a quantum Hamiltonian) do not meet these conditions. In the field of scientific instruments, digital twins typically have a very large number (e.g., dozens, hundreds, or even thousands) of parameters that have high-dimensional, ambiguous, or otherwise counterintuitive or poorly understood interdependencies with each other. The prior art cannot be confidently applied to such cases. After all, if it is not clear which parameters depend on which other parameters, then one does not know in what order to calibrate the parameters according to the prior art.

[0032] In addition, such prior art is susceptible to what the present inventor refers to as the problem of parameter ambiguity. Specifically, the present inventor has recognized that when calibrating the parameters of a digital twin by performing experiments or tests on a scientific instrument, the parameters of the digital twin may have or appear to have an inherent symmetry such that multiple different instantiations of the parameter state of the digital twin correspond to or appear to correspond to the same obtained or measured experimental or test result. In fact, in some cases, the mathematical or physics-based underlying theory of the digital twin may allow for the existence of multiple parameter state instantiations that actually correspond to a given experimental result; in other cases, noisy experimental measurements may make it seem as if multiple parameter state instantiations correspond to a given experimental result. In any case, while there is only one way to calibrate the parameters of the digital twin to match the true physical state of the scientific instrument, there may be more than one way to calibrate the parameters of the digital twin to be consistent with the experimental or test result.

[0033] The prior art has not recognized or mentioned the problem of parameter ambiguity. In addition, as recognized by the present inventor, the prior art cannot reliably handle or solve the problem of parameter ambiguity. In fact, when there are multiple possible instantiations of the parameter state that correspond to an experimental or test result, calibration using a dependency ordering of different test metrics for different parameters can be seen as prematurely or arbitrarily locking the digital twin into one of these possibilities without sufficient reason (e.g., without even being aware or knowing that there are multiple other possibilities to choose from). In other words, which of these multiple possible instantiations of the parameter state is determined or selected by the prior art can be a function of an arbitrary order of calibrating the parameters of the digital twin.

[0034] For example, assume that the parameter state of a digital twin of a scientific instrument consists of a first parameter and a second parameter. Assume that the first parameter is calibrated before the second parameter. In this case, an experiment corresponding to or customized for the first parameter can be performed on the scientific instrument, and the result of this experiment can suggest or indicate that the first parameter should be assigned the value A. Next, some other experiment corresponding to or customized for the second parameter can be performed on the scientific instrument, and the result of this experiment, when taken in conjunction with the calibrated value A of the first parameter, can suggest or indicate that the second parameter should be assigned the value B. Thus, when the first parameter is calibrated before the second parameter, the parameter state of the digital twin can be calibrated to the A-B instantiation. Now, assume instead that the second parameter is calibrated before the first parameter. In this case, an experiment corresponding to or customized for the second parameter can be performed on the scientific instrument, and the result of this experiment can suggest or indicate that the second parameter should be assigned the value C. Next, some other experiment corresponding to or customized for the first parameter can be performed on the scientific instrument, and the result of this experiment, when taken in conjunction with the calibrated value C of the second parameter, can suggest or indicate that the first parameter should be assigned the value D. Thus, when the second parameter is calibrated before the first parameter, the parameter state of the digital twin can be calibrated to the D-C instantiation. This example shows that there can be multiple possible parameter state instantiations (e.g., the A-B instantiation and the D-C instantiation) that are consistent with the experiments performed on the scientific instrument, and this example also shows that which of these multiple possible parameter state instantiations is ultimately selected or chosen by the prior art can depend on the order in which the digital twin parameters are calibrated. In cases where the parameter interdependencies are unknown or not well understood from the start, making the selected or chosen instantiation of the parameter state of the digital twin depend on the calibration order can be considered unreasonable, arbitrary, or particularly troublesome.

[0035] In addition, the present inventors have recognized that using different test metrics for different parameters exacerbates this problem of not being able to handle parameter ambiguity. After all, using different experimental procedures, tests, or metrics for different parameters can be considered as treating different parameters inconsistently with each other (e.g., in practice, different metrics are typically measured with different uncertainties or resolution errors). Introducing such inconsistencies into the digital twin calibration process may reduce the likelihood of locating the true physical state of the scientific instrument among the multiple possible parameter state instantiations.

[0036] As recognized by the present inventors, for any given digital twin, one or more specific experimental tests, observations, or metrics that are not affected by the problem of parameter ambiguity can be attempted to be identified in an ad-hoc manner. However, in terms of time consumption, effort, and blind experimentation, such an ad-hoc approach would be prohibitively expensive (e.g., for a particular digital twin representing a certain type of scientific instrument, it may take a significant amount of effort to experimentally identify tests, observations, or metrics that are not affected by the problem of parameter ambiguity; unfortunately, for a different digital twin representing a different type of scientific instrument, this effort and experimentation would have to be repeated from scratch). Additionally, such an ad-hoc approach may not even work because there is no general or universal guarantee that any given digital twin has at least some experimental tests, observations, or metrics that are not affected by the problem of parameter ambiguity. Furthermore, such an ad-hoc approach would be considered more like avoiding or evading the problem of parameter ambiguity in the first place rather than addressing or solving the problem of parameter ambiguity when it occurs.

[0037] Accordingly, a system or technique that can ameliorate one or more of these technical problems may be desirable.

[0038] The various embodiments described herein can address one or more of these technical problems. One or more embodiments described herein can include a system, computer-implemented method, apparatus, or computer program product that can facilitate improved calibration of a digital twin of a scientific instrument. In particular, the various embodiments described herein can relate to calibrating the parameters of a digital twin of a scientific instrument via the implementation of a full-state Bayesian filter. More specifically, the full-state Bayesian filter can be an algorithm or procedure that involves performing multiple calibration iterations on the digital twin. As described herein, each calibration iteration can involve (e.g., via Bayes' rule) performing a recursive Bayesian update on the entire parameter state of the digital twin. Additionally, such a recursive Bayesian update can be based on observables that can be exhibited by the scientific instrument, simulated by the digital twin, and shared or identical across multiple calibration iterations. At least for this reason, such an observable can be referred to as an iteration-shared observable.

[0039] Note how such embodiments can differ from the prior art. As described above, the prior art calibrates the parameters of the digital twin individually in order of dependence (e.g., updating the geometry parameters in the first iteration; then updating the stiffness and yield strength parameters in the second iteration; and finally updating the damping coefficient parameter in the third iteration). In stark contrast, the various embodiments described herein do not involve calibrating the parameters of the digital twin individually or independently. Instead, the various embodiments described herein involve progressively updating, adjusting, or otherwise modifying the numerical values of all of the parameters of the digital twin during each calibration iteration (e.g., if the various embodiments described herein were implemented in the above example, the geometry parameters, the stiffness and yield strength parameters, and the damping coefficient parameter would all be progressively updated in the first iteration; the geometry parameters, the stiffness and yield strength parameters, and the damping coefficient parameter would all be progressively updated again in a subsequent iteration; the geometry parameters, the stiffness and yield strength parameters, and the damping coefficient parameter would all be progressively updated again in a further subsequent iteration). Additionally, as described above, the prior art utilizes different test metrics or observables in each calibration iteration (e.g., directly measuring the geometry parameters in the first calibration iteration; estimating the stiffness and yield strength parameters via a load-displacement metric in the second calibration iteration; and estimating the damping coefficient parameter via a vibration or initial condition metric in the third calibration iteration). In stark contrast, the various embodiments described herein do not involve utilizing such different test metrics or observables. Instead, the various embodiments described herein involve utilizing a single, common, or general test metric or observable across all calibration iterations (e.g., if the various embodiments described herein were implemented in the above example, each calibration iteration would involve performing a load-displacement test, or each calibration iteration would instead involve performing a vibration or initial condition test).

[0040] In various aspects, these differences between the prior art and the various embodiments described herein can enable such embodiments to overcome the various drawbacks or problems encountered by such prior art.

[0041] In fact, as described above, because the prior art performs calibration in the order of parameter interdependencies, such prior art can only be used for physically intuitive digital twin parameters with easily understandable interdependencies (e.g., geometric shape parameters, stiffness and yield strength parameters, and damping coefficient parameters). In sharp contrast, the various embodiments described herein do not require or necessitate any prior information about the interdependencies between digital twin parameters. In fact, as described herein, all digital twin parameters can be progressively updated in each calibration iteration without considering their interdependencies, rather than updating different parameters in different iterations in the order of their dependencies. Thus, the various embodiments described herein can be considered to be generalizable to digital twin parameters with highly complex, cumbersome, or unknown interactions or interdependencies with each other (e.g., optical aberration coefficients; or numerical elements like quantum Hamiltonians).

[0042] Furthermore, as described above, because the prior art performs calibration in the order of parameter interdependencies using different test metrics or observables for different subsets of parameters, such prior art cannot adequately address the problem of parameter ambiguity. In fact, it is possible for multiple different instantiations of the parameter state of a digital twin associated with a scientific instrument to be consistent with the experimental results obtained from the scientific instrument (especially for digital twin parameters with unknown or highly cumbersome interdependencies). However, only one of these multiple different instantiations can be considered to actually match the true physical state of the scientific instrument. By calibrating parameters individually in the order of dependencies and by using different test metrics or observables for different parameters, the prior art arbitrarily locks itself into one of these multiple different instantiations without even realizing that such multiple different instantiations are possible. In sharp contrast, the various embodiments described herein do not use different observables for different parameters to individually calibrate parameters in the order of dependencies. Instead, the various embodiments described herein can progressively adjust all parameters during each calibration iteration (rather than adjusting different parameters during different iterations), and this adjustment can be based on an experimental metric or observable that is common or general throughout all calibration iterations (e.g., the same experiment or test can be performed on the scientific instrument in each calibration iteration, rather than performing different experiments or tests during each iteration or for different parameters). The inventors have experimentally verified that this calibration procedure correctly calibrates digital twin parameters even in the presence of the parameter ambiguity problem. In other words, the prior art tends to inaccurately or incorrectly calibrate digital twin parameters in the presence of the parameter ambiguity problem, while the various embodiments described herein accurately or correctly calibrate digital twin parameters in the presence of the parameter ambiguity problem.

[0043] Accordingly, the various embodiments described herein can be considered to facilitate improved digital twin calibration for scientific instruments.

[0044] The various embodiments described herein can be considered computerized tools (e.g., any suitable combination of computer-executable hardware or computer-executable software) that can be electronically installed on or otherwise mounted relative to a scientific instrument and can facilitate improved digital twin calibration for the scientific instrument. In various aspects, such a computerized tool can include an access component, a calibration component, or a post-calibration component.

[0045] In various embodiments, the scientific instrument can be any suitable computerized device that can electronically capture, measure, or otherwise record any suitable electronic information carrying clinical or laboratory significance (e.g., can be a mass spectrometer, can be an electron microscope). In any case, the scientific instrument can include a set of controllable instrument settings. In various aspects, the controllable instrument settings can be any suitable configurable hardware or software characteristic of the scientific instrument that can be directly controlled, adjusted, or changed in response to an electronic instruction or command received from a user or technician of the scientific instrument (e.g., can be a voltage or current setting controlled by the user of the scientific instrument, a temperature setting controlled by the user of the scientific instrument, or an actuator setting controlled by the user of the scientific instrument).

[0046] In various embodiments, there can be a digital twin corresponding to or otherwise associated with the scientific instrument. In various aspects, the digital twin can be any suitable combination of any suitable mathematical or physics-based models that can numerically, computationally, or analytically predict, forecast, or otherwise simulate how the scientific instrument or any suitable part thereof will respond to any given usage scenario. More specifically, the digital twin can include a parameter state, a set of input variables, and a set of output variables.

[0047] In various cases, the parameter state can include any suitable number of parameters, and each of these parameters can be any suitable mathematical quantity that can represent any suitable characteristic, attribute, or property (whether physical or theoretical) of a scientific instrument (e.g., the geometry of the scientific instrument, the material properties of the scientific instrument, the aberration coefficients of the scientific instrument). In various cases, the characteristics, attributes, or properties of the scientific instrument represented by the parameter state can be non-transient (e.g., avoid drifting over time) or transient (e.g., drift over time). In various cases, the characteristics, attributes, or properties of the scientific instrument represented by the parameter state can be indirectly affected or influenced by a set of controllable instrument settings (e.g., the damping coefficient of the scientific instrument can be indirectly changed by changing one or more controllable motor settings of the scientific instrument). However, it can be the case that none of such characteristics, attributes, or properties can be directly controlled by the set of controllable instrument settings (e.g., the scientific instrument does not have a button or knob that allows direct or explicit selection of a desired damping coefficient; if it did, calibration of the damping coefficient parameter would be straightforward).

[0048] In various aspects, the set of input variables can include any suitable number of input variables. In various cases, an input variable can be any suitable mathematical quantity that can represent any suitable detail or aspect of a usage scenario that the scientific instrument may encounter (e.g., can represent or quantify the mass or chemical composition of a laboratory sample that can be scanned or analyzed by the scientific instrument).

[0049] In various cases, the set of output variables can include any suitable number of output variables. In various aspects, an output variable can be any suitable mathematical quantity that can represent any suitable detail or aspect of the scientific instrument that is desired to be calculated, predicted, or tracked (e.g., the marginal wear or degradation accumulated by the scientific instrument or any of its parts in response to a given usage scenario).

[0050] In various aspects, the set of input variables can be collectively regarded as the operands of the digital twin, the parameter state can be collectively regarded as the operator defining the digital twin, and the set of output variables can be calculated or operated on by applying the parameter state to the set of input variables in a mathematical manner (e.g., via any suitable mathematical function or its composition). Thus, the set of input variables can be assigned any numerical values that define or describe any given usage scenario, and the behavior or response of the scientific instrument to that given usage scenario can be simulated, predicted, or forecasted by applying the parameter state to the set of input variables. However, such simulation, prediction, or forecast may be inaccurate if the parameter state of the digital twin does not closely match the true physical state of the scientific instrument.

[0051] Accordingly, it is desirable to calibrate or synchronize the parametric state of the digital twin with the true physical state of the scientific instrument. As described herein, a computerized tool can facilitate such calibration or synchronization.

[0052] In various embodiments, an access component of the computerized tool can electronically access a set of controllable instrument settings or a digital twin. That is, the access component can electronically interface or communicate with the set of controllable instrument settings or with the digital twin such that the access component can act as a conduit through which other components of the computerized tool can electronically interact with the set of controllable instrument settings or with the digital twin (e.g., send electronic commands to the set of controllable instrument settings or the digital twin, read electronic signals from the set of controllable instrument settings or the digital twin).

[0053] In various embodiments, a calibration component of the computerized tool can electronically synchronize the parametric state of the digital twin to the true physical state of the scientific instrument. In other words, the calibration component can identify a calibration instantiation of the parametric state that is estimated or predicted to be an acceptable approximation of the true physical state of the scientific instrument (e.g., within any suitable threshold margin of similarity of that true physical state). In particular, the calibration component can achieve this by performing or implementing a full state Bayesian filter on both the scientific instrument and the digital twin.

[0054] In various aspects, a full-state Bayesian filter may include multiple calibration iterations that are performed sequentially. That is, the multiple calibration iterations may include an initial calibration iteration and a final calibration iteration. In various cases, the multiple calibration iterations may be driven by an iterative shared observable. In various cases, the iterative shared observable may be, represent, or otherwise refer to any suitable measurable or observable behavior that: can be demonstrated by a scientific instrument during or after running a standardized or repeatable usage scenario; and can be simulated (e.g., predicted, output, computed) by a digital twin. In some cases, the iterative shared observable may be any one of an output variable set computed in response to a standardized or repeatable usage scenario. In other cases, the iterative shared observable may instead be derived from any one of an output variable set computed in response to a standardized or repeatable usage scenario. In any case, each calibration iteration among the multiple calibration iterations may involve: operating or running the scientific instrument according to the standardized or repeatable usage scenario so as to measure or observe the behavior of the scientific instrument with respect to the iterative shared observable; simulating the standardized or repeatable usage scenario on the digital twin so as to predict the behavior of the scientific instrument with respect to the iterative shared observable; and progressively updating all parameter states of the digital twin (e.g., all parameters of the digital twin, not just a subset of the digital twin) via recursive application of Bayes' rule based on the error between the measured or observed behavior demonstrated by the scientific instrument and the simulated behavior predicted by the digital twin. Note that the adjective "iterative shared" may be considered appropriate as the iterative shared observable is used across all multiple calibration iterations (e.g., one metric or observable is shared or common for all calibration iterations).

[0055] More specifically, the first or initial calibration iteration among the multiple calibration iterations may be performed as follows.

[0056] In various aspects, the true physical state of the scientific instrument may be unknown. However, despite this unknown true physical state, the calibration component may cause, instruct, or otherwise command the scientific instrument to operate or run according to a standardized or repeatable usage scenario. During or after such operation or run, the calibration component may electronically measure (or may instruct the scientific instrument to electronically measure) the behavior of the scientific instrument with respect to the iterative shared observable. This may be referred to as the first measurement observation of the iterative shared observable.

[0057] In various cases, the calibration component can randomly sample or randomly select a first plurality of parameter state instantiations of the digital twin from an unconstrained or unrestricted version of the state space of the digital twin. More specifically, each parameter of the digital twin can be considered a mathematical quantity that can originate from a corresponding domain of values, and all of these corresponding domains can together be considered to form the state space of the digital twin, and any suitable number of instantiations of the parameter state can be randomly selected from such a state space.

[0058] In various cases, the calibration component can use each parameter state instantiation among the first plurality of parameter state instantiations to cause, instruct, or otherwise command the digital twin to run or simulate a standardized or repeatable usage scenario. In other words, for each given parameter state instantiation among the first plurality of parameter state instantiations, assuming that the true physical state of the scientific instrument matches the given parameter state instantiation, the digital twin can predict how the scientific instrument will behave with respect to iteratively shared observables. This can produce a first plurality of simulated observation results of the iteratively shared observables.

[0059] In various aspects, for each given parameter state instantiation among the first plurality of parameter state instantiations, the calibration component can calculate a corresponding weight based on the error (e.g., mean absolute error (MAE), mean squared error (MSE), cross-entropy error) between the first measured observation results of the iteratively shared observables and any simulated observation results corresponding to that given parameter state instantiation among the first plurality of simulated observation results. In various cases, the weight assigned to any given parameter state instantiation can be the reciprocal of any error calculated for that given parameter state instantiation, can be based on the complement of that error, or can otherwise be inversely proportional to that error.

[0060] In various cases, the calibration component can delete, remove, or otherwise discard any parameter state instantiation among the first plurality of parameter state instantiations that has a weight below any suitable threshold weight value. This can produce a first plurality of remaining parameter state instantiations. In various cases, the calibration component can calculate one or more variances of the first plurality of remaining parameter state instantiations, and the calibration component can compare these one or more variances with any suitable threshold variance value.

[0061] If these one or more variances satisfy (e.g., are below) the threshold variance value, the calibration component can calculate a calibrated parameter state instantiation based on the first plurality of remaining parameter state instantiations. In particular, the calibrated parameter state instantiation can be equal to or otherwise based on the weighted average of the first plurality of remaining parameter state instantiations. As described above, the calibrated parameter state instantiation can be considered an acceptable or sufficient approximation of the true physical state of the scientific instrument. Thus, in various cases, the calibration component can cause the parameters of the digital twin to assume any numerical values indicated by the calibrated parameter state instantiation.

[0062] Conversely, if these one or more variances fail to meet (e.g., are above) a threshold variance value, the calibration component may apply any suitable active setting adjustment (e.g., knob turn, button press, joystick displacement, graphical user interface invocation or click) to any one of the controllable instrument settings in the set of controllable instrument settings of the scientific instrument. In various aspects, the calibration component may modify each parameter state instantiation in the first plurality of remaining parameter state instantiations respectively according to the active setting adjustment, and such modification may result in a first plurality of modified remaining parameter state instantiations. In fact, as described above, the characteristics, attributes, or properties of the scientific instrument represented by the parameters of the digital twin cannot be directly or explicitly controlled or selected by the set of controllable instrument settings (e.g., otherwise, calibration would be straightforward). However, these characteristics, attributes, or properties can still be indirectly affected by the set of controllable instrument settings in a known or understood manner or pattern. In other words, any given change to the set of controllable instrument settings can be expected to cause a commensurate, related, or otherwise known change to these characteristics, attributes, or properties of the scientific instrument (e.g., the unknown true physical state of the scientific instrument). In yet other words, changing a particular controllable instrument setting by a particular amount or percentage can be known or expected to change one or more of these characteristics, attributes, or properties by one or more corresponding amounts or percentages. Thus, for each given remaining parameter state instantiation in the first plurality of remaining parameter state instantiations, the calibration component can adjust, modify, or change the given remaining parameter state instantiation in any way that would be expected to be caused by the active setting adjustment, thereby resulting in a corresponding one of the first plurality of modified remaining parameter state instantiations.

[0063] Now, the second calibration iteration among the multiple calibration iterations can be performed as follows.

[0064] In various aspects, the true physical state of the scientific instrument may still be unknown, but this true physical state may have been changed in some known or expected way by the aforementioned active setting adjustment. As described above, the calibration component may cause, instruct, or otherwise command the scientific instrument to operate or run according to a standardized or repeatable usage scenario, and the calibration component may accordingly measure (or may instruct the scientific instrument to measure) the behavior of the scientific instrument with respect to the iterative common observable. This may be referred to as the second measurement observation of the iterative common observable.

[0065] Now, in various cases, the calibration component can randomly sample or randomly select a second plurality of parameter state instantiations from the state space of the digital twin. However, rather than sampling or selecting from an unconstrained or infinite-valued version of the state space, the calibration component can sample or select from a progressively constrained or restricted version of the state space. In various aspects, this progressively constrained or restricted version of the state space can be instantiated based on the remaining parameter states of the first plurality of modifications. More specifically, the remaining parameter state instantiations of the first plurality of modifications can be regarded as Bayesian evidence, which can be used in recursive Bayesian updates to tighten or shrink the state space of the digital twin. The practical effect of such recursive Bayesian updates can be to make the second plurality of parameter state instantiations numerically resemble any region or span close to or local to the remaining parameter states of the first plurality of modifications of the state space, numerically close to these regions or spans, or otherwise selected from these regions or spans (e.g., the second plurality of parameter state instantiations cannot be sampled or selected from regions or spans of the state space that do not contain or are far from the remaining parameter states of the first plurality of modifications).

[0066] In various cases, then, a second calibration iteration can be performed as described above. That is, the calibration component can: cause the digital twin to run or simulate a standardized or repeatable usage scenario using each parameter state instantiation of the second plurality of parameter state instantiations, thereby generating a second plurality of simulated observations of iterative common observables; calculate a corresponding weight for each parameter state instantiation of the second plurality of parameter state instantiations based on the error between the second measured observations of the iterative common observables and the second plurality of simulated observations of the iterative common observables; delete any parameter state instantiations of the second plurality of parameter state instantiations that have weights below a threshold weight value, thereby generating a second plurality of remaining parameter state instantiations; and compare one or more variances of the second plurality of remaining parameter state instantiations with a threshold variance value. If these one or more variances satisfy (e.g., are below) the threshold variance value, the calibration component can calculate a calibrated parameter state instantiation based on the second plurality of remaining parameter state instantiations (e.g., equal to or otherwise based on the weighted average of the second plurality of remaining parameter state instantiations). Conversely, if these one or more variances fail to satisfy (e.g., are above) the threshold variance value, the calibration component can apply any other active setting adjustment to the set of controllable instrument settings of the scientific instrument, and the calibration component can proportionally modify the second plurality of remaining parameter state instantiations based on this active setting adjustment, thereby generating a second plurality of modified remaining parameter state instantiations. Then, the calibration component can proceed to a third calibration iteration of the full-state Bayesian filter.

[0067] The full-state Bayesian filter can proceed in such a way that each calibration iteration progressively narrows, tightens, or shrinks the state space of the digital twin until a threshold variance value is met. When the threshold variance value is finally met in a given calibration iteration, the calibration parameter state instantiation can be equal to the weighted average of any number of remaining parameter state instantiations calculated during that given calibration iteration.

[0068] In various embodiments, the post-calibration component of a computerized tool can facilitate, perform, or otherwise initiate any suitable electronic action based on the calibration or synchronization performed by the calibration component.

[0069] As a non-limiting example, the post-calibration component can generate any suitable electronic notification in response to the calibration or synchronization performed by the calibration component, the electronic notification indicating that the digital twin is now ready to accurately simulate, predict, or forecast the future behavior of a scientific instrument. In some cases, the post-calibration component can transmit the electronic notification to any suitable computing device. In other cases, the post-calibration component can visually present the electronic notification on any suitable computer screen or monitor.

[0070] As another non-limiting example, the post-calibration component can cause, command, or otherwise instruct the digital twin to actually simulate, predict, or forecast the future behavior of a scientific instrument. For example, a user or technician of a scientific instrument can identify a specific use scenario via an interface of the scientific instrument (e.g., keyboard, keypad, touchscreen) and can request a prediction of how the scientific instrument will respond or behave in that specific use scenario. In various aspects, the post-calibration component can thus assign any numerical values corresponding to or defining the specific use scenario to a set of input variables (e.g., these numerical values can be provided by the user or technician via the interface), and the post-calibration component can cause, instruct, or command the digital twin to apply the calibration parameter state instantiation to the set of input variables, thereby generating specific numerical values for a set of output variables. In various cases, the post-calibration component can visually present any one of these specific numerical values of the set of output variables on any suitable computer screen or monitor for visibility to the user or technician. In this way, the digital twin can be considered to accurately or reliably simulate how the scientific instrument will behave in or during a specific use scenario.

[0071] The various embodiments described herein can be used to solve problems that are highly technical in nature (e.g., facilitating improved calibration of digital twins for scientific instruments), non-abstract, and not executable by humans as a set of mental acts, using either hardware or software. Additionally, some of the processes performed can be executed by a dedicated computer (e.g., a digital twin configured to model the behavior of a mass spectrometer or an electron microscope) to perform defined actions related to a scientific instrument.

[0072] For example, such defined actions may include: synchronizing the parametric state of a digital twin with the physical state of a scientific instrument by a device operatively coupled to a processor and via the execution of a full-state Bayesian filter; and generating, by the device and in response to the synchronization, an electronic alert indicating that the digital twin is ready to predict the behavior of the scientific instrument. In some cases, such defined actions may include: predicting, by the device and via running the digital twin after synchronization, how the scientific instrument will respond to a proposed usage scenario. In various cases, the full-state Bayesian filter may include a set of calibration iterations, each of which may include a Bayesian update of all parametric states based on iteratively shared observables that can be exhibited by the scientific instrument and simulated by the digital twin.

[0073] Such defined actions are inherently computerized. In fact, scientific instruments (such as chromatographs, mass spectrometers, and electron microscopes) are highly technical computerized devices that include specific computerized hardware (e.g., temperature sensors, pressure sensors, voltage sensors, ion beam emitters, ion focusing lenses, mass analyzers, ion detectors, beam apertures, fluid valves). Without a computer, scientific instruments and the operations performed by them cannot be achieved in any reasonable or feasible way by human thinking or by humans using pen and paper. Similarly, a digital twin (as implied by the word "digital" in its name) is also an inherently computerized or virtual construct that is used to electronically predict or simulate the future behavior of a scientific instrument. Without a computer, a digital twin cannot be facilitated or executed in any reasonable or feasible way by human thinking or by humans using pen and paper. In addition, the action of calibrating or synchronizing the parameters of a digital twin with the true physical state of a scientific instrument is an inherently software- and hardware-based iterative procedure that involves calculating the error between the simulated behavior output by the digital twin and the actual real-world behavior measured by or regarding the scientific instrument. This calibration or synchronization cannot be performed in any meaningful, reasonable, or feasible way by human thinking or by humans using pen and paper.

[0074] In addition, the various embodiments described herein may incorporate various teachings related to improving digital twin calibration for scientific instruments into practical applications. As explained above, the prior art facilitates such calibration by separately calibrating digital twin parameters in a sequence of dependencies (e.g., first geometric shape parameters, then stiffness and yield strength parameters, and then finally damping coefficient parameters) and using different observables for different parameters (e.g., direct measurements for geometric shape parameters; load-displacement observations for stiffness and yield point parameters; vibration observations for damping coefficient parameters). Such prior art is only applicable to simple, physically intuitive parameters with well-understood or clear interdependencies; such prior art cannot be applied to non-physically intuitive parameters with ambiguous or unclear interdependencies. Additionally, such prior art cannot confidently or reliably address the issue of parameter ambiguity. In fact, there may be multiple possible parameter state instantiations consistent with a given experimental finding (e.g., this is often the case when dealing with cumbersome, non-physically intuitive parameters such as aberration coefficients or quantum Hamiltonians). However, the dependency-based sequence of calibration and the use of different observables for different parameters enable the prior art to identify or determine any one of these multiple possible parameter state instantiations. In other words, changing the order of calibration and thus changing when to query which observables can change which one of these multiple possible parameter state instantiations is identified or determined. At least for these reasons, the prior art can be considered to encounter various technical problems.

[0075] The various embodiments described herein may help to improve one or more of such technical problems. In particular, the various embodiments described herein may relate to calibrating the parameters of a digital twin to the physical state of a scientific instrument by leveraging or implementing a full-state Bayesian filter. As described herein, a full-state Bayesian filter may be an algorithm or procedure that involves performing a series of calibration iterations on a scientific instrument and a digital twin, where each calibration iteration may involve performing a recursive Bayesian update (e.g., posterior calculation based on evidence and prior) on the entire parameter state of the digital twin (e.g., for all parameters), thus using the adjective "full-state". In addition, the full-state Bayesian filter may be driven by iteratively shared observables that may be exhibited during the operation of the scientific instrument and may be simulated or predicted by the digital twin. In various cases, the iteratively shared observables may be queried or tested throughout or across all sequences of calibration iterations, thus using the adjective "iteratively shared". As described herein, the implementation of a full-state Bayesian filter driven by iteratively shared observables may cause each calibration iteration to progressively tighten, shrink, or narrow the available state space of the digital twin until one or more variances of that state space drop below any suitable threshold. At this point, the tightened, shrunk, or narrowed state space may be considered to indicate or identify the true physical state of the scientific instrument. Different from the prior art, the various embodiments described herein may be generalized to any suitable digital twin parameters, regardless of how cumbersome, ambiguous, or unknown the interdependencies of these digital twin parameters may be. In fact, the various embodiments described herein do not require or even use knowledge of parameter interdependencies. Also different from the prior art, the various embodiments described herein are not disturbed, misled, or otherwise hindered by the problem of parameter ambiguity. In fact, although there are multiple possible solutions arising from physics-based fundamental theories or from noisy measurements, the inventors have experimentally verified that a full-state Bayesian filter driven by iteratively shared observables can correctly or accurately identify the true physical state of a scientific instrument. In addition, the various embodiments described herein may be considered a generalizable or universal parameter calibration technique that may be applied to any suitable digital twin representing any suitable type of scientific instrument. This contrasts with ad hoc techniques that require a large number of custom or specialized experiments for different digital twins representing different types of scientific instruments. At least for these reasons, the various embodiments described herein may be considered a specific and tangible technical improvement in the field of digital twins. Accordingly, the various embodiments described herein are certainly eligible as a useful and practical application of a computer.

[0076] In addition, the various embodiments described herein can control tangible devices in the real world based on the disclosed teachings. For example, the various embodiments described herein can electronically activate, deactivate, or otherwise actuate the real-world hardware (e.g., ion beam emitters, ion focusing lenses, carrier fluid valves / pumps) of real-world scientific instruments (e.g., mass spectrometers, electron microscopes).

[0077] Figure 1 An example non-limiting block diagram of a scientific instrument module 102 in accordance with various embodiments described herein is illustrated.

[0078] In various embodiments, the scientific instrument module 102 can be implemented by a circuit (e.g., including electrical or optical components) such as a programmed computing device. The logic components of the scientific instrument module 102 can be included in a single computing device or can be distributed across multiple computing devices that communicate with each other as appropriate. Reference is made herein Figure 24 and Figure 26 to examples of computing devices that can implement the scientific instrument module 102, either alone or in combination, and reference is made to Figure 25 and Figure 27 to examples of systems or networks of interconnected computing devices across which the scientific instrument module 102 can be implemented among one or more of the computing devices in the computer device.

[0079] The scientific instrument module 102 may include a first logic component 104, a second logic component 106, and a third logic component 108. As used herein, the term "logic component" may include a device that performs a set of operations associated with logic. For example, any of the logic elements included in the scientific instrument module 102 may be implemented by one or more computing devices programmed with instructions to cause one or more processing devices of the computing device to perform the associated set of operations. In a particular embodiment, the logic element may include one or more non-transitory computer-readable media having instructions thereon that, when executed by one or more processing devices in one or more computing devices, cause the one or more computing devices to perform the associated set of operations. As used herein, the term "module" may refer to a collection of one or more logic elements that together perform functions associated with the module. Different logic elements in a module may take the same form or may take different forms. For example, some of the logic components in a module may be implemented by a programmed general-purpose processing device, while other logic components in the module may be implemented by an application-specific integrated circuit (ASIC). As another example, different logic elements in a module may be associated with different instruction sets executed by one or more processing devices. A module may omit one or more logic elements shown in the associated figure; for example, when a module will perform a subset of the operations discussed herein with reference to the module, the module may include a subset of the logic elements depicted in the associated figure.

[0080] In various embodiments, there may be a scientific instrument corresponding to the scientific instrument module 102. In various aspects, a scientific instrument may be any suitable computerized device that can electronically measure some scientific-related, clinical-related, or research-related characteristics, properties, or attributes of an analytical sample (e.g., a collection of known or unknown mixtures, compounds, or substances). As a non-limiting example, a scientific instrument may be a mass spectrometer operatively coupled to a gas chromatograph or a liquid chromatograph. In such a case, the scientific instrument may measure or determine the ion spectrum of the analytical sample (e.g., relative ion abundance as a function of mass-to-charge ratio). As another non-limiting example, a scientific instrument may be a scanning electron microscope. In such a case, the scientific instrument may measure or determine the surface topography of the analytical sample. As yet another non-limiting example, a scientific instrument may be a transmission electron microscope. In such a case, the scientific instrument may measure or determine the internal structural details of the analytical sample. As a more general non-limiting example, a scientific instrument may be any suitable type of charged particle microscope (e.g., some types of microscopes may use non-electron ion beams to capture images).

[0081] In various embodiments, the first logic component 104 may have access to a digital twin of the scientific instrument. In various aspects, the digital twin can be any suitable collection of mathematical or physics-based formulas, equations, or models that can simulate, predict, or otherwise forecast any suitable behavioral aspect of the scientific instrument. As a non-limiting example, the digital twin may include any suitable formula, equation, or model that can predict the amount of wear or degradation experienced or accumulated by the scientific instrument or any of its parts. As another non-limiting example, when given an analytical sample, the digital twin may include any suitable formula, equation, or model that can predict scientific-related, clinically-related, or research-related measurements that would be obtained by the scientific instrument if the scientific instrument were to analyze the given analytical sample.

[0082] In various embodiments, the second logic component 106 may calibrate or synchronize the parameter state of the digital twin to the physical state of the scientific instrument. After all, without such calibration or synchronization, the digital twin may not accurately simulate, forecast, or predict the behavior of the scientific instrument. In various aspects, the second logic component 106 may facilitate such calibration or synchronization by leveraging or implementing a full-state Bayesian filter. In various cases, the full-state Bayesian filter may be driven by iteratively shared observables. In various cases, the iteratively shared observables can be any suitable state-dependent characteristic that: can be exhibited or observed by the scientific instrument during operation or run; and can be simulated, predicted, or forecasted by the digital twin. In particular, the full-state Bayesian filter may include a series of calibration iterations, where each calibration iteration may involve: operating or running the scientific instrument, thereby generating a measurement or observation behavior corresponding to the iteratively shared observable; operating or running the digital twin, thereby generating a simulation or forecast behavior corresponding to the iteratively shared observable; and recursively updating the entire (and thus "full-state") parameter state of the digital twin based on the error calculated between the measurement or observation behavior and the simulation or forecast behavior.

[0083] In various embodiments, the third logic component 108 may facilitate any suitable electronic action in response to the completion or execution of the second logic component 106. As a non-limiting example, the third logic component 108 may include notifying a user or technician of a scientific instrument electronically (e.g., via a visual reproduction on an electronic display) that the digital twin has been calibrated and is thus ready or prepared to accurately simulate, predict, or forecast the future behavior of the scientific instrument. As another non-limiting example, the third logic component 108 may include receiving electronically (e.g., via an electronic interface of the scientific instrument) user input that identifies or indicates a proposed usage scenario of the scientific instrument (e.g., analyzing one or more user-specified analysis samples in a user-specified time frame using a user-specified instrument configuration), and the third logic component 108 may further include executing or running the digital twin on the proposed usage scenario, thereby simulating, predicting, or forecasting how the scientific instrument will behave or respond to the proposed usage scenario.

[0084] Accordingly, the scientific instrument module 102 may facilitate improved calibration of the digital twin for the scientific instrument.

[0085] Figure 2 is an example non-limiting flowchart of a computer-implemented method 200 according to various embodiments described herein. The operations of the computer-implemented method 200 may be used in any suitable context to perform any suitable operation (e.g., may be executed by or used in conjunction with any of the various modules, computing devices, or graphical user interfaces described with respect to Figure 1 , Figure 23 , Figure 24 , Figure 25 , Figure 26 and Figure 27 ). In Figure 2 , the operations are each illustrated once in a particular order, but the operations may be reordered or repeated as needed and as appropriate (e.g., different operations may be performed in parallel in suitable circumstances).

[0086] In various aspects, action 202 may include performing a first operation of accessing the digital twin of the scientific instrument. In various cases, the first logic component 104 may perform or otherwise facilitate action 202.

[0087] In various cases, action 204 may include performing a second operation of synchronizing the parameter state of the digital twin to the physical state of the scientific instrument via the execution of a full-state Bayesian filter. In various aspects, the full-state Bayesian filter may be driven by, or otherwise based on, an iterative shared observable that may be exhibited by the scientific instrument and simulated by the digital twin. In various cases, the second logic component 106 may perform or otherwise facilitate action 204.

[0088] In various aspects, operation 206 may include a third operation of performing the behavior of a predictive science instrument or otherwise notifying a user that the digital twin is ready for such prediction in response to synchronizing a parameter state with a physical state. In various cases, the third logic component 108 may perform or otherwise facilitate operation 206.

[0089] Accordingly, the computer-implemented method 200 may facilitate improved calibration of a digital twin for a science instrument.

[0090] Figure 3 A block diagram of an example non-limiting science instrument 302 that may facilitate improved calibration of a digital twin in accordance with one or more embodiments described herein is illustrated.

[0091] In various aspects, the science instrument 302 may be as described above. That is, the science instrument 302 may be any suitable computerized device that can electronically measure any suitable scientific-related, clinical-related, or research-related characteristic, property, or quality of any suitable analytical sample. As a non-limiting example, the science instrument 302 may be a mass spectrometer equipped or outfitted with gas chromatography or liquid chromatography. In such a case, the science instrument 302 may utilize its constituent hardware (e.g., syringe, oven heating column, carrier fluid valve or pump, ion beam emitter, ion optical device lens or shield, mass analyzer) to electronically determine the chemical composition or constitution of any given analytical sample. As another non-limiting example, the science instrument 302 may be a scanning or transmission electron microscope. In such a case, the science instrument 302 may utilize its constituent hardware (e.g., electron source, anode, condenser lens, condenser aperture, scanning coil, objective lens, objective aperture, deflector, condenser, astigmatism correction device, electron detector, X-ray detector, actuatable sample stage) to electronically determine or map the surface structure or internal structure of any given analytical sample.

[0092] Although not explicitly shown in the figure, the science instrument 302 may be electronically integrated with any suitable human-machine interface device, which may be remote from or local to the science instrument 302. Accordingly, a user or technician associated with the science instrument 302 may interact with or otherwise control the science instrument 302. Some non-limiting examples of the human-machine interface device may be a keyboard of the science instrument 302, a keypad of the science instrument 302, a touch screen of the science instrument 302, or a voice command system of the science instrument 302.

[0093] In various cases, as shown, the science instrument 302 may include a set of controllable instrument settings 304 and a digital twin 306. In various cases, the digital twin 306 may include a parameter state 308, a set of input variables 310, and a set of output variables 312. RegardingFigure 4 Describes various non - limiting aspects.

[0094] Figure 4 Illustrates an example non - limiting block diagram showing a set of controllable instrument settings 304, parameter status 308, a set of input variables 310, and a set of output variables 312 according to one or more embodiments described herein.

[0095] In various aspects, for any suitable positive integer n, the set of controllable instrument settings 304 can include n settings: controllable instrument setting 304(1) through controllable instrument setting 304(n). In various cases, each controllable instrument setting in the set of controllable instrument settings 304 can be any suitable hardware-related or software-related characteristic of the scientific instrument 302 that can direct or affect how the scientific instrument 302 operates or functions with respect to any given analytical sample and can be selectively configured or otherwise controlled by a user or technician (e.g., via interaction with a human-machine interface device of the scientific instrument 302). That is, controllable instrument setting 304(1) can be regarded as the first user-configurable hardware-related or software-related characteristic that affects how the scientific instrument 302 operates or functions on an analytical sample, and controllable instrument setting 304(n) can be regarded as the nth user-configurable hardware-related or software-related characteristic that affects how the scientific instrument 302 operates or functions on an analytical sample. As a non-limiting example, any one of the controllable instrument settings in the set of controllable instrument settings 304 can be a user-configurable voltage or current setting that can allow the user or technician to control an electrode of the scientific instrument 302 to selectively increase or decrease the voltage or current within or applied by the scientific instrument 302. As another non-limiting example, any one of the controllable instrument settings in the set of controllable instrument settings 304 can be a user-configurable temperature setting that can allow the user or technician to control a heater (e.g., oven, heating coil) or cooler (e.g., cooling fan, heat pump, refrigerator) of the scientific instrument 302 to selectively increase or decrease the temperature within or applied by the scientific instrument 302. As yet another non-limiting example, any one of the controllable instrument settings in the set of controllable instrument settings 304 can be a user-configurable radiation setting that can allow the user or technician to control a radiation source (e.g., electron emitter, ion beam emitter) of the scientific instrument 302 to selectively increase or decrease the radiation level within or applied by the scientific instrument 302. As yet another non-limiting example, any one of the controllable instrument settings in the set of controllable instrument settings 304 can be a user-configurable mechanical actuator setting that can allow the user or technician to control a mechanical actuator (e.g., motor, sample stage, iris diaphragm) of the scientific instrument 302 to selectively move the mechanical actuator. As yet another non-limiting example, any one of the controllable instrument settings in the set of controllable instrument settings 304 can be a user-configurable optics setting that can allow the user or technician to control an optical element (e.g., optical lens, optical deflector) of the scientific instrument 302 to selectively change the optical quality (e.g., focal spot size or position, astigmatism, defocus) applied by the scientific instrument 302.

[0096] In various aspects, the digital twin 306 can be any suitable collection or set of any suitable mathematical or physics-based models that can collectively simulate, predict, or otherwise forecast any suitable behavioral details or aspects of the scientific instrument 302. As a non-limiting example, the digital twin 306 can include any suitable mass continuity equations, inequalities, or formulas that in some way pertain to the scientific instrument 302. As another non-limiting example, the digital twin 306 can include any suitable energy balance equations, inequalities, or formulas that in some way pertain to the scientific instrument 302. As yet another non-limiting example, the digital twin 306 can include any suitable heat transfer equations, inequalities, or formulas that in some way pertain to the scientific instrument 302. As yet another non-limiting example, the digital twin 306 can include any suitable fluid flow equations, inequalities, or formulas that in some way pertain to the scientific instrument 302. As yet another non-limiting example, the digital twin 306 can include any suitable dynamics or kinematics equations, inequalities, or formulas that in some way pertain to the scientific instrument 302. As another non-limiting example, the digital twin 306 can include any suitable Newtonian mechanics or quantum mechanics equations, inequalities, or formulas that in some way pertain to the scientific instrument 302. As yet another non-limiting example, the digital twin 306 can include any suitable corrosion or degradation equations, inequalities, or formulas that in some way pertain to the scientific instrument 302.

[0097] In any case, the digital twin 306 can be regarded as a collection or set of mathematical or physics-based models that can simulate, predict, or forecast something regarding the scientific instrument 302, and these mathematical or physics-based models can be regarded as consisting of a parameter state 308, a set of input variables 310, and a set of output variables 312. In particular, the parameter state 308 can be regarded as defining the operators or coefficients of these mathematical or physics-based models, the set of input variables 310 can be regarded as the operands or independent variables of these mathematical or physics-based models, and the set of output variables 312 can be regarded as the simulation, prediction, or forecast results calculated by these mathematical or physics-based models.

[0098] In various aspects, for any suitable positive integer m, the parameter state 308 of the digital twin 306 can include m parameters: parameter 308(1) to parameter 308(m). In various cases, each parameter of the parameter state 308 can be any suitable mathematical quantity that can represent a corresponding physical or theoretical characteristic, attribute, or property of the scientific instrument 302. For example, parameter 308(1) can be a scalar, vector, matrix, tensor, or any suitable combination thereof that can represent the first physical or theoretical characteristic, attribute, or property of the scientific instrument 302. Similarly, parameter 308(m) can be a scalar, vector, matrix, tensor, or any suitable combination thereof that can represent the mth physical or theoretical characteristic, attribute, or property of the scientific instrument 302. As some non-limiting examples, any parameter of the parameter state 308 can represent: the length of the scientific instrument 302 or any of its parts or components; the width of the scientific instrument 302 or any of its parts or components; the height of the scientific instrument 302 or any of its parts or components; the thickness of the scientific instrument 302 or any of its parts or components; the radius of curvature of the scientific instrument 302 or any of its parts or components; the mass or density of the scientific instrument 302 or any of its parts or components; the stiffness of the scientific instrument 302 or any of its parts or components; the damping coefficient of the scientific instrument 302 or any of its parts or components; the resistance of the scientific instrument 302 or any of its parts or components; the impedance of the scientific instrument 302 or any of its parts or components; the thermal resistance of the scientific instrument 302 or any of its parts or components; the thermal conductivity of the scientific instrument 302 or any of its parts or components; the heat capacity of the scientific instrument 302 or any of its parts or components; the optical opacity of the scientific instrument 302 or any of its parts or components; the optical aberration coefficients of the scientific instrument 302 or any of its parts or components (e.g., defocus coefficient, second-order astigmatism coefficient); the decoherence time of the scientific instrument 302 or any of its parts or components; or the quantum Hamiltonian elements of the scientific instrument 302 or any of its parts or components.

[0099] In some cases, any parameter of the parameter state 308 can represent a physical or theoretical characteristic, attribute, or property that is expected to be non-transient, constant, or fixed. That is, it is expected that the value of such a physical or theoretical characteristic, attribute, or property will not drift, decay, or otherwise change over time or with the use of the scientific instrument 302. However, in other cases, any parameter of the parameter state 308 can represent a physical or theoretical characteristic, attribute, or property that is expected to be transient, non-fixed, or non-constant. That is, it is expected that the value of such a physical or theoretical characteristic, attribute, or property will slowly or rapidly drift, decay, or otherwise change over time or with the use of the scientific instrument 302.

[0100] Note that in various aspects, it may be the case that any parameter of parameter state 308 cannot be directly or explicitly controlled or selected by any of the controllable instrument settings in the set of controllable instrument settings 304. Despite the lack of this direct and explicit control, however, it may be the case that any parameter of parameter state 308 can be indirectly affected or altered by one or more of the controllable instrument settings in the set of controllable instrument settings 304. As a non-limiting example, assume that the scientific instrument 302 is an electron microscope and assume that the parameter state 308 includes an aberration coefficient parameter. Although the aberration coefficient can be regarded as a useful theoretical property that helps to quantitatively describe the optical performance or behavior of the electron microscope, there is no aberration coefficient knob, slider, joystick, physical button, or software button that is manufactured in or with the electron microscope that allows for the explicit, direct selection of a particular desired aberration coefficient value. Nevertheless, the configurable knobs, sliders, joysticks, physical buttons, or software buttons that the electron microscope does have (e.g., a defocus knob, an astigmatism correction knob, a specimen stage actuator joystick, a voltage knob, a temperature knob) can still indirectly affect the aberration coefficient of the electron microscope (e.g., adjusting any of the defocus knob, the astigmatism correction device knob, the specimen stage actuator joystick, the voltage knob, or the temperature knob can increase or decrease the aberration coefficient of the electron microscope).

[0101] In various aspects, for any suitable positive integer s, the set of input variables 310 of the digital twin 306 can include s variables: input variable 310(1) to input variable 310(s). In various cases, each input variable in the set of input variables 310 can be any suitable mathematical quantity that can represent any suitable dimension, feature, detail, or aspect of a usage scenario that the scientific instrument 302 may encounter or experience. For example, input variable 310(1) can be a scalar, vector, matrix, tensor, or any suitable combination thereof that can represent a first dimension, feature, detail, or aspect of a usage scenario that can be encountered by the scientific instrument 302. Similarly, input variable 310(s) can be a scalar, vector, matrix, tensor, or any suitable combination thereof that can represent the s-th dimension, feature, detail, or aspect of a usage scenario that can be encountered by the scientific instrument 302. As some non-limiting examples, any one of the input variables in the set of input variables 310 can represent: the mass or density of an analysis sample that the scientific instrument 302 can analyze; the chemical composition of an analysis sample that the scientific instrument 302 can analyze; the crystal structure of an analysis sample that the scientific instrument 302 can analyze; the absorption coefficient of an analysis sample that the scientific instrument 302 can analyze; the preheating time provided or allocated to the scientific instrument 302 for analyzing the analysis sample; the operating time provided or allocated to the scientific instrument 302 for analyzing the analysis sample; the cooling time provided or allocated to the scientific instrument 302 for analyzing the analysis sample; the maximum or minimum voltage level that will be used by the scientific instrument 302 for analyzing the analysis sample; the maximum or minimum radiation level that will be used by the scientific instrument 302 for analyzing the analysis sample; the maximum or minimum fluid flow rate that will be used by the scientific instrument 302 for analyzing the analysis sample; or the maximum or minimum temperature level that will be used by the scientific instrument 302 for analyzing the analysis sample.

[0102] In various aspects, for any suitable positive integer t, the set of output variables 312 of the digital twin 306 can include t variables: output variable 312(1) to output variable 312(t). In various cases, each output variable in the set of output variables 312 can be any suitable mathematical quantity that can represent any suitable characteristic, property, quality, feature, detail, or aspect of the behavior that the scientific instrument 302 is expected to simulate, predict, or forecast. For example, output variable 312(1) can be a scalar, vector, matrix, tensor, or any suitable combination thereof that can represent the characteristics, properties, qualities, features, details, or aspects of the first simulation, prediction, or forecast of the scientific instrument 302. Similarly, output variable 312(t) can be a scalar, vector, matrix, tensor, or any suitable combination thereof that can represent the characteristics, properties, qualities, features, details, or aspects of the t-th simulation, prediction, or forecast of the scientific instrument 302. As a non-limiting example, any one of the output variables in the set of output variables 312 can represent an electronic measurement that can be captured or generated by the scientific instrument 302 in response to any given usage scenario. For example, if the scientific instrument 302 is a mass spectrometer, one or more output variables can represent the material composition results or percentages that can be output by the scientific instrument 302. As another example, if the scientific instrument 302 is an electron microscope, one or more output variables can represent an image (e.g., a pixel array or a voxel array) that can be output by the scientific instrument 302. As another non-limiting example, any one of the output variables in the set of output variables 312 can represent the total or marginal amount of degradation or wear that the scientific instrument 302 or any part thereof can experience or accumulate in response to any given usage scenario. As yet another non-limiting example, any one of the output variables in the set of output variables 312 can represent the total or marginal amount of fuel, calibration agent, or electricity that can be consumed by the scientific instrument 302 in response to any given usage scenario. As yet another non-limiting example, any one of the output variables in the set of output variables 312 can represent the amount of service life (e.g., expressed in hours or number of runs) that the scientific instrument 302 will retain or leave in response to any given usage scenario.

[0103] Accordingly, the digital twin 306 can simulate, predict, or forecast how the scientific instrument 302 will respond to any particular usage scenario. Specifically, the set of input variables 310 can be assigned any suitable numerical values corresponding to or otherwise defining that particular usage scenario, the parameter state 308 can be applied (e.g., according to any mathematical function, operation, or formula that makes up the digital twin 306) to the set of input variables 310, and the set of output variables 312 can be equal to the calculation result (e.g., product, difference, sum, quotient) of such application.

[0104] Return reference Figure 3, Note that the digital twin 306 can accurately or reliably simulate, predict, or forecast the behavior of the scientific instrument 302 only when the parameter state 308 is assigned a value that closely matches the true physical state of the scientific instrument 302. However, this true physical state may be unknown and may vary with the set of controllable instrument settings 304. Accordingly, it may be desirable to calibrate or synchronize the parameter state 308 of the digital twin 306 with this unknown true physical state of the scientific instrument 302. As described herein, the scientific instrument 302 may include a system 314 that facilitates such calibration or synchronization.

[0105] In various aspects, the system 314 may include a processor 316 (e.g., a computer processing unit, a microprocessor) and a non-transitory computer-readable memory 318 that is operatively or operationally or communicatively connected or coupled to the processor 316. The non-transitory computer-readable memory 318 may store computer-executable instructions that, when executed by the processor 316, may cause the processor 316 or other components of the system 314 (e.g., the access component 320, the calibration component 322, the post-calibration component 324) to perform one or more actions. In various embodiments, the non-transitory computer-readable memory 318 may store computer-executable components (e.g., the access component 320, the calibration component 322, the post-calibration component 324), and the processor 316 may execute these computer-executable components.

[0106] In various embodiments, the system 314 may include an access component 320. In various aspects, the access component 320 may communicate electronically or otherwise interact electronically with the set of controllable instrument settings 304 or the digital twin 306 (e.g., transmit electronic instructions or commands to the set of controllable instrument settings or the digital twin, receive electronic data from the set of controllable instrument settings or the digital twin). Accordingly, any other component of the system 314 may communicate or interact with the set of controllable instrument settings 304 or with the digital twin 306 through or via the access component 320 (e.g., the access component 320 may act as an intermediary between any other component of the system 314 and the set of controllable instrument settings 304 or the digital twin 306). However, this is merely a non-limiting example. In other cases, the access component 320 may be omitted, and any other component of the system 314 may communicate or interact directly with the set of controllable instrument settings 304 or the digital twin 306.

[0107] In various embodiments, the system 314 may include a calibration component 322. In various aspects, as described herein, the calibration component 322 may electronically calibrate or synchronize the parameter state 308 of the digital twin 306 with the unknown true physical state of the scientific instrument 302 by utilizing a full-state Bayesian filter.

[0108] In various embodiments, system 314 may include a post-calibration component 324. In various cases, as described herein, the post-calibration component 324 may perform or initiate any suitable electronic action in response to the parameter state 308 of the digital twin 306 being successfully calibrated or synchronized with the unknown true physical state of the scientific instrument 302.

[0109] Figure 5 A block diagram of an example non-limiting scientific instrument is illustrated that includes a full-state Bayesian filter and calibration parameter state instantiation that can facilitate improved digital twin calibration in accordance with one or more embodiments described herein.

[0110] In various embodiments, the calibration component 322 may electronically identify a calibration parameter state instantiation 504 for the digital twin 306 based on the full-state Bayesian filter 502. In various aspects, the calibration parameter state instantiation 504 may be regarded as or indicate any particular numerical value of a parameter of the parameter state 308 that is determined or inferred to be equal to or otherwise (e.g., within any suitable similarity margin) close to the unknown true physical state of the scientific instrument 302. In various cases, the full-state Bayesian filter 502 may be an iterative procedure or algorithm that utilizes the actual behavior exhibited by the scientific instrument 302, the simulated behavior predicted by the digital twin 306, and recursive Bayesian updates to ultimately identify the calibration parameter state instantiation 504. Regarding Figures 6 to 12 Various non-limiting aspects are described.

[0111] Figure 6 A block diagram of an example non-limiting full-state Bayesian filter 502 is illustrated that shows in accordance with one or more embodiments described herein.

[0112] In various embodiments, as described above, the full-state Bayesian filter 502 may be an iterative procedure or algorithm. In particular, the full-state Bayesian filter 502 may include a plurality of calibration iterations 602. In various aspects, for any suitable positive integer z > 1, the plurality of calibration iterations 602 may include z iterations: calibration iteration 602(1) through calibration iteration 602(z). In various cases, the plurality of calibration iterations 602 may be performed in sequence. Thus, calibration iteration 602(1) may be regarded as the first, initial, or starting iteration among the plurality of calibration iterations 602, and calibration iteration 602(z) may be regarded as the last, final, or ending iteration among the plurality of calibration iterations 602.

[0113] In various cases, multiple calibration iterations 602 can be driven by an iterative shared observable 604. In other words, the actions performed within each of the multiple calibration iterations 602 can depend on the iterative shared observable 604. In various aspects, the iterative shared observable 604 can correspond to or otherwise be associated with a standard, repeatable, or baseline usage scenario of the scientific instrument 302. More specifically, the iterative shared observable 604 can be any suitable measurable, trackable, recordable, or otherwise quantifiable detail, concept, aspect, or behavior that: can be exhibited or generated by the scientific instrument 302 during or after running the standard, repeatable, or baseline usage scenario; and can be simulated, predicted, or forecasted by the digital twin 306. Thus, the iterative shared observable 604 can be considered to be any suitable state-dependent quantity (e.g., one or more scalars, one or more vectors, one or more matrices, one or more tensors, or any suitable combination thereof) that is equal to or equivalent to any one of the output variables in the set of output variables 312 or equal to or equivalent to any suitable function of one or more of the output variables in the set of output variables 312.

[0114] As a non-limiting example, assume that the scientific instrument 302 is an electron microscope. In this case, the scientific instrument 302 may be able to measure or generate a convergent beam electron diffraction (CBED) pattern by running or operating on an analysis sample, where the CBED pattern can be considered to depict a two-dimensional pixel array of an internal or external portion of the analysis sample. In various aspects, the standard, repeatable, or baseline usage scenario can be to capture a CBED pattern of some defined analysis sample (e.g., amorphous carbon) using some defined instrument setting configuration (e.g., using defined or baseline optics, voltage, or power settings). Additionally, when given a CBED pattern of an analysis sample, the Fourier transform of the given CBED pattern can be computed. Thus, in various cases, the iterative shared observable 604 can refer to applying the Fourier transform to a CBED pattern generated by scanning an amorphous carbon sample according to defined or baseline optics, voltage, or power settings.

[0115] In any case, the iterative shared observables 604 can be used, exploited, or otherwise referenced in each of the multiple calibration iterations 602 (rather than using different observables in each iteration). In particular, each of the multiple calibration iterations 602 can involve: running the scientific instrument 302 according to a standard, repeatable, or baseline usage scenario, thereby allowing measurement or observation of the behavior of the scientific instrument 302 with respect to the iterative shared observables 604; simulating the standard, repeatable, or baseline usage scenario on the digital twin 306, thereby allowing prediction of the behavior of the scientific instrument 302 with respect to the iterative shared observables 604; and incrementally updating the entire parameter state 308 (rather than just a subset of the parameter state) via Bayes' theorem based on the error between the measured or observed behavior and the predicted behavior. Regarding Figures 7 to 12 various non-limiting aspects are described.

[0116] Figures 7 to 12 Example non-limiting block diagrams 700, 800, 900, 1000, 1100, and 1200 are illustrated that show iterations of the full-state Bayesian filter 502 in accordance with one or more embodiments described herein. In particular, Figures 7 to 12 it can be considered to show how, for any suitable positive integer j≥1, the j-th calibration iteration of the multiple calibration iterations 602 can be performed or facilitated by the calibration component 322.

[0117] First, consider Figure 7 . In various aspects, the scientific instrument 302 can be considered to have some unknown true physical state at the start of the j-th calibration iteration. As described above, this true physical state can be indirectly affected by the set of controllable instrument settings 304, but this true physical state cannot be directly or explicitly selected or indicated by the set of controllable instrument settings 304 (e.g., otherwise, calibration would be trivial).

[0118] In various situations, regardless of the true physical state of the scientific instrument 302, the calibration component 322 can electronically instruct, electronically command, or otherwise electronically cause the scientific instrument 302 to perform, operate, or otherwise act according to a standard, repeatable, or baseline usage scenario corresponding to the iterative common observable 604. Such performance, operation, or action can cause the scientific instrument 302 to exhibit some measurable behavior regarding the iterative common observable 604, and the calibration component 322 can electronically record this measurable behavior, or otherwise cause an electronic record of this measurable behavior. This can produce a measurement observation 702. In other words, the measurement observation 702 can be any suitable mathematical quantity (e.g., scalar, vector, matrix, tensor, or any suitable combination thereof) representing how the scientific instrument 302 behaves regarding the iterative common observable 604 during or attributable to the standard, repeatable, or baseline usage scenario of the j-th calibration iteration.

[0119] As a non-limiting example, as described above, assume that the scientific instrument 302 is an electron microscope, and assume that the iterative common observable 604 refers to the Fourier-transformed CBED pattern of an amorphous carbon sample. In this case, the amorphous carbon sample can be inserted into the scientific instrument 302 (e.g., placed on the sample stage of the scientific instrument 302); the calibration component 322 can cause the scientific instrument 302 to capture or generate the CBED pattern of the amorphous carbon sample during the j-th calibration iteration; the calibration component 322 can apply a Fourier transform to the captured CBED pattern; and any mathematical quantity produced by this Fourier transform can be regarded as the measurement observation 702.

[0120] Now, consider Figure 8 In various aspects, the parameter state 308 can be regarded as having or corresponding to a state space from which a particular instantiation of the parameter state 308 can be selected. More specifically, the parameter state 308 can include m different parameters; each of such m different parameters can be regarded as corresponding to a respective domain; and all such domains can be collectively regarded as forming or defining the state space of the digital twin 306. As a non-limiting example, the parameter 308(1) can be regarded as corresponding to a first domain, where this first domain can be regarded as the set of all possible numerical values or combinations of numerical values that can be assigned to the parameter 308(1). As another non-limiting example, the parameter 308(m) can be regarded as corresponding to the m-th domain, where this m-th domain can be regarded as the set of all possible numerical values or combinations of numerical values that can be assigned to the parameter 308(m). In any case, all such m domains can be regarded as jointly constituting the state space of the digital twin 306.

[0121] In various cases, calibration component 322 may probabilistically sample the state space of digital twin 306 during the j-th calibration iteration. This may result in a plurality of sampled parameter state instantiations 802. In various cases, for any suitable positive integer x > 1, the plurality of sampled parameter state instantiations 802 may include x instantiations: sampled parameter state instantiation 802(1) through sampled parameter state instantiation 802(x). In various aspects, each sampled parameter state instantiation among the plurality of sampled parameter state instantiations 802 may be a particular version of parameter state 308, where the parameters of the particular version are assigned particular numerical values probabilistically selected from their respective domains.

[0122] As a non-limiting example, sampled parameter state instantiation 802(1) may be considered a first particular version of parameter state 308. Thus, sampled parameter state instantiation 802(1) may include a first particular numerical value probabilistically selected from the domain of parameter 308(1) (e.g., if parameter 308(1) represents the damping coefficient of scientific instrument 302 that can vary from a minimum damping value to a maximum damping value, then sampled parameter state instantiation 802(1) may include a particular damping coefficient value probabilistically picked from the interval formed by these minimum and maximum damping values). Similarly, sampled parameter state instantiation 802(1) may include an m-th particular numerical value probabilistically selected from the domain of parameter 308(m) (e.g., if parameter 308(m) represents the aberration coefficient of scientific instrument 302 that can vary from a minimum aberration value to a maximum aberration value, then sampled parameter state instantiation 802(1) may include a particular aberration coefficient value probabilistically picked from the interval formed by these minimum and maximum aberration values).

[0123] As another non-limiting example, sampled parameter state instantiation 802(x) may be considered the x-th particular version of parameter state 308. Thus, sampled parameter state instantiation 802(x) may include a first particular numerical value probabilistically selected from the domain of parameter 308(1). Similarly, sampled parameter state instantiation 802(x) may include an m-th particular numerical value probabilistically selected from the domain of parameter 308(m).

[0124] In various aspects, when j = 1 (e.g., during the first, beginning, or initial calibration iteration), the calibration component 322 may probabilistically select multiple sampled parameter state instantiations 802 from an unconstrained or infinite-valued version of the state space of the digital twin 306. In contrast, when j > 1 (e.g., during any non-initial calibration iteration), the calibration component 322 may probabilistically select multiple sampled parameter state instantiations 802 from a constrained or restricted version of the state space of the digital twin 306, where this constrained or restricted version of the state space may be calculated by performing recursive Bayesian updates on any version of the state space sampled during the (j - 1) calibration iteration (e.g., the previous calibration iteration).

[0125] More specifically, recall that recursive Bayesian updates may involve calculating a posterior distribution given both a prior distribution and evidence (e.g., via Bayes' theorem, Bayes' rule, or Bayesian inference). In various aspects, the calibration component 322 may select multiple sampled parameter state instantiations 802 from any suitable prior distribution of the state space of the digital twin 306. If j = 1, this prior distribution may be any desired or defined distribution of the state space, such as a uniform distribution (e.g., which would give all possible parameter state instantiations in the state space an equal likelihood of being selected by the calibration component 322) or a normal or Gaussian distribution (e.g., which would give parameter state instantiations with extreme or deviant parameter values a lower likelihood of being selected by the calibration component 322). However, if j > 1, this prior distribution may be equal to or otherwise based on any posterior distribution that was calculated during the (j - 1) calibration iteration. This will be further elucidated below with respect to Figure 12 be further clarified.

[0126] In any case, the calibration component 322 may probabilistically select multiple sampled parameter state instantiations 802 from the state space of the digital twin 306.

[0127] In various aspects, the calibration component 322 may electronically command, electronically instruct, or otherwise electronically cause the digital twin 306 to simulate a corresponding run of a standard, repeatable, or baseline usage scenario corresponding to the iterative shared observable 604 for each of the multiple sampled parameter state instantiations 802. Assuming that the true physical state of the scientific instrument 302 matches these physical states indicated by each of the multiple sampled parameter state instantiations 802, this simulation may enable the digital twin 306 to predict or forecast what measurable or observable behavior the scientific instrument 302 will exhibit with respect to the iterative shared observable 604. This may result in multiple simulated observations 804.

[0128] For example, the calibration component 322 may run the digital twin 306 on the sampling parameter state instantiation 802(1). That is, assuming that the true physical state of the scientific instrument 302 matches any particular numerical value indicated by the sampling parameter state instantiation 802(1), the calibration component 322 may instruct, command, or cause the digital twin 306 to simulate, predict, or forecast how the scientific instrument 302 will behave with respect to the iterative common observable 604 during a standard, repeatable, or baseline usage scenario. The simulated observation result 804(1) may be any suitable mathematical quantity (e.g., a scalar, vector, matrix, tensor, or any suitable combination thereof) representing such simulated, predicted, or forecast behavior. Again considering the non-limiting example above, in which the scientific instrument 302 is an electron microscope and in which the iterative common observable 604 refers to the Fourier-transformed CBED pattern of an amorphous carbon sample. In this case, if the true physical state of the scientific instrument 302 matches the numerical value indicated by the sampling parameter state instantiation 802(1), the digital twin 306 may simulate, predict, or forecast the synthetic CBED image of the amorphous carbon sample that the scientific instrument 302 will capture. The calibration component 322 may correspondingly apply a Fourier transform to the simulated, predicted, or forecast CBED pattern; and any mathematical quantity generated by such Fourier transform may be regarded as the simulated observation result 804(1).

[0129] Similarly, the calibration component 322 may run the digital twin 306 on the sampling parameter state instantiation 802(x). That is, assuming that the true physical state of the scientific instrument 302 matches any particular numerical value indicated by the sampling parameter state instantiation 802(x), the calibration component 322 may instruct, command, or cause the digital twin 306 to simulate, predict, or forecast how the scientific instrument 302 will behave with respect to the iterative common observable 604 during a standard, repeatable, or baseline usage scenario. The simulated observation result 804(x) may be any suitable mathematical quantity (e.g., a scalar, vector, matrix, tensor, or any suitable combination thereof) representing such simulated, predicted, or forecast behavior. Once again considering the non-limiting example above, in which the scientific instrument 302 is an electron microscope and in which the iterative common observable 604 refers to the Fourier-transformed CBED pattern of an amorphous carbon sample. In this case, if the true physical state of the scientific instrument 302 matches the numerical value indicated by the sampling parameter state instantiation 802(x), the digital twin 306 may simulate, predict, or forecast the synthetic CBED image of the amorphous carbon sample that the scientific instrument 302 will capture. The calibration component 322 may correspondingly apply a Fourier transform to the simulated, predicted, or forecast CBED pattern; and any mathematical quantity generated by such Fourier transform may be regarded as the simulated observation result 804(x).

[0130] In various cases, the simulated observation results 804(1) to the simulated observation results 804(x) can be jointly regarded as forming a plurality of simulated observation results 804.

[0131] Now, consider Figure 9 . In various aspects, the calibration component 322 can electronically calculate or operate on a plurality of weights 902 based on the measured observation result 702 and the plurality of simulated observation results 804(1). More specifically, the calibration component 322 can calculate the corresponding error between the measured observation result 702 and each of the plurality of simulated observation results 804, and the plurality of weights 902 can be any suitable function of these corresponding errors.

[0132] As a non-limiting example, the calibration component 322 can calculate any suitable error (e.g., MAE, MSE, cross-entropy error, Euclidean distance error) between the measured observation result 702 and the simulated observation result 804(1). In various aspects, then, the calibration component 322 can calculate the weight 902(1) based on this error. In various instances, the weight 902(1) can be any suitable scalar, the magnitude of which can range from any suitable minimum value (e.g., 0) to any suitable maximum value (e.g., 1). In various aspects, the weight 902(1) can be any suitable function of the error between the measured observation result 702 and the simulated observation result 804(1), such that the magnitude of the weight 902(1) is inversely proportional to the magnitude of this error (e.g., the magnitude of the weight 902(1) can increase as the error between the measured observation result 702 and the simulated observation result 804(1) decreases; the magnitude of the weight 902(1) can decrease as the error between the measured observation result 702 and the simulated observation result 804(1) increases). For example, in some cases, the weight 902(1) can be equal to or otherwise based on the reciprocal of the error between the measured observation result 702 and the simulated observation result 804(1). In other cases, the weight 902(1) can be equal to or otherwise based on the complement of the error between the measured observation result 702 and the simulated observation result 804(1). In any case, since the weight 902(1) can be calculated based on the simulated observation result 804(1), and since the simulated observation result 804(1) can be obtained based on the sampling parameter state instantiation 802(1), the weight 902(1) can be regarded as indicating the likelihood that the sampling parameter state instantiation 802(1) matches the true physical state of the scientific instrument 302.

[0133] As another non - limiting example, the calibration component 322 can compute any suitable error (e.g., MAE, MSE, cross - entropy error, Euclidean distance error) between the measured observation 702 and the simulated observation 804(x). In various aspects, then, the calibration component 322 can compute the weight 902(x) based on this error. As described above, the weight 902(x) can be any suitable scalar, the magnitude of which can range from any suitable minimum value (e.g., 0) to any suitable maximum value (e.g., 1). Also as described above, the weight 902(x) can be any suitable function of the error between the measured observation 702 and the simulated observation 804(x), such that the magnitude of the weight 902(x) is inversely proportional to the magnitude of this error (e.g., the weight 902(x) can be equal to or otherwise based on the reciprocal or complement of the error between the measured observation 702 and the simulated observation 804(x)). In any case, since the weight 902(x) can be computed based on the simulated observation 804(x), and since the simulated observation 804(x) can be obtained based on the sampled parameter state instantiation 802(x), the weight 902(x) can be regarded as indicating the likelihood that the sampled parameter state instantiation 802(x) matches the true physical state of the scientific instrument 302.

[0134] In various cases, the weights 902(1) through 902(x) can be regarded as jointly forming a plurality of weights 902, and the plurality of weights 902 can be regarded as respectively corresponding to the plurality of sampled parameter state instantiations 802.

[0135] Now, consider Figure 10 . In various aspects, the calibration component 322 can electronically delete, electronically discard, or otherwise electronically ignore any sampled parameter state instantiation among the plurality of sampled parameter state instantiations 802 that has a weight that fails to meet (e.g., is below) any suitable threshold weight value. In other words, the calibration component 322 can eliminate any sampled parameter state instantiation among the plurality of sampled parameter state instantiations 802 that is not sufficiently weighted. In various cases, this can result in a plurality of remaining parameter state instantiations 1002 and a plurality of remaining weights 1004.

[0136] In various cases, for any suitable positive integer y < x, the plurality of remaining parameter state instantiations 1002 can include y instantiations: remaining parameter state instantiation 1002(1) to remaining parameter state instantiation 1002(y). In various aspects, the plurality of remaining weights 1004 can respectively correspond to the plurality of remaining parameter state instantiations 1002. Thus, since the plurality of remaining parameter state instantiations 1002 can include y instantiations, the plurality of remaining weights 1004 can likewise include y weights: remaining weight 1004(1) to remaining weight 1004(y). As a non-limiting example, the remaining parameter state example 1002(1) can be any one of the plurality of sampled parameter state instantiations 802 having a weight higher than a threshold weight value, and the remaining weight 1004(1) can be that weight (e.g., the weight of the remaining parameter state instantiation 1002(1)). As another non-limiting example, the remaining parameter state example 1002(y) can be any one of the plurality of sampled parameter state instantiations 802 having a weight higher than a threshold weight value, and the remaining weight 1004(y) can be that weight (e.g., the weight of the remaining parameter state instantiation 1002(y)).

[0137] Now, in various aspects, the calibration component 322 can electronically calculate or compute a per-parameter variance based on the plurality of remaining parameter state instantiations 1002. In fact, as described above, each of the plurality of remaining parameter state instantiations 1002 can include m specific numerical values respectively corresponding to the m parameters of the parameter state 308. Thus, for each given parameter of the parameter state 308, the calibration component 322 can compute the variance of the specific numerical values assigned to that given parameter across the plurality of remaining parameter state instantiations 1002, thereby producing m variances. Such m variances can be collectively regarded as the per-parameter variance of the plurality of remaining parameter state instantiations 1002.

[0138] As a non-limiting example, each of the plurality of remaining parameter state instantiations 1002 can include a specific numerical value assigned to parameter 308(1). Thus, the plurality of remaining parameter state instantiations 1002 can be regarded as collectively having a total of y specific numerical values assigned to parameter 308(1). In various cases, the calibration component 322 can calculate the variance of these y specific numerical values. This variance can be regarded as the first per-parameter variance of the plurality of remaining parameter state instantiations 1002.

[0139] As another non - limiting example, each of the plurality of remaining parameter state instantiations 1002 may include a specific numerical value assigned to parameter 308(m). Thus, the plurality of remaining parameter state instantiations 1002 may be considered to collectively have a total of y specific numerical values assigned to parameter 308(m). In various cases, the calibration component 322 may compute the variance of these y specific numerical values. This variance may be considered the m - th per - parameter variance of the plurality of remaining parameter state instantiations 1002.

[0140] In any case, the calibration component 322 may compute the per - parameter variances across the plurality of remaining parameter state instantiations 1002. In various aspects, the calibration component 322 may compare each of these per - parameter variances with any suitable threshold variance value. Figure 11 Illustrated is how the j - th calibration iteration may proceed if each of these per - parameter variances satisfies (e.g., is below) the threshold variance value. In contrast, Figure 12 Illustrated is how the j - th calibration iteration may proceed if at least one of these per - parameter variances fails to satisfy (e.g., is above) the threshold variance value.

[0141] Consider Figure 11 . If each of the per - parameter variances satisfies the threshold variance value, the calibration component 322 may electronically compute or calculate the calibration parameter state instantiation 1002 based on the plurality of remaining parameter state instantiations 504. In particular, the calibration parameter state instantiation 504 may be equal to or otherwise based on the weighted average of the plurality of remaining parameter state instantiations 1002. That is, each of the plurality of remaining parameter state instantiations 1002 may be multiplied by the corresponding one of the plurality of remaining weights 1004 (e.g., remaining parameter state instantiation 1002(1) may be multiplied by remaining weight 1004(1); remaining parameter state instantiation 1002(y) may be multiplied by remaining weight 1004(y)), resulting in y products, and the calibration parameter state instantiation 504 may be equal to the arithmetic mean of such y products.

[0142] In other words, when the per - parameter variance satisfies the threshold variance value, the plurality of remaining parameter state instantiations 1002 may be considered to be closely grouped or clustered around the unknown true physical state of the scientific instrument 302, and that unknown true physical state may be estimated accordingly by weighted averaging.

[0143] Now, consider Figure 12。If at least one of the per-parameter variances fails to meet the threshold variance value, in various respects, the calibration component 322 may electronically perform (or otherwise cause to be electronically performed) an active setting adjustment 1202 on the scientific instrument 302. In various cases, the active setting adjustment 1202 can be any suitable change or modification to one or more of the controllable instrument settings in the set 304 of controllable instrument settings (e.g., any suitable button press or button selection, any suitable degree of any suitable knob turn, any suitable amount of any suitable slider shift, any suitable amount in any suitable direction of any suitable joystick displacement). Thus, the active setting adjustment 1202 can be considered to change one or more user-configurable aspects of the scientific instrument 302 (e.g., voltage, current, temperature, stage height, focal spot size, flow rate, radiation level) by one or more known amounts.

[0144] In fact, although the true physical state of the scientific instrument 302 may still be unknown at this time, the active setting adjustment 1202 can be considered to change the true physical state of the scientific instrument in some known way, by some known amount, or otherwise in some known direction. Note that in a subsequent calibration iteration (e.g., the (j + 1) calibration iteration), the calibration component 322 may cause or instruct the scientific instrument 302 to operate or perform a standard, repeatable, or baseline usage scenario in or using this newly changed but still unknown true physical state.

[0145] Returning to reference the jth calibration iteration, in various cases, calibration component 322 may electronically modify or adjust each of the plurality of remaining parameter state instantiations 1002 according to or otherwise based on the active setting adjustment 1202. After all, as explained above, the true physical state of the scientific instrument 302 (e.g., any characteristic, property, or quality of the scientific instrument 302 represented by the parameter state 308) cannot be directly or explicitly controlled or selected by the set of controllable instrument settings 304 (again, otherwise calibration would be trivial). However, the true physical state can still be indirectly affected by the set of controllable instrument settings 304 in a known manner or via a known relationship. For example, assume that the active setting adjustment 1202 shifts one or more of the controllable instrument settings in the set of controllable instrument settings 304 by one or more specific amounts or percentages, or causes the one or more controllable instrument settings to shift in one or more corresponding directions. In various cases, such a shift may be known or expected to shift one or more corresponding characteristics, properties, or qualities of the true physical state that makes up the scientific instrument 302 by one or more corresponding amounts or percentages, or causes the one or more corresponding characteristics, properties, or qualities to shift in one or more corresponding directions. Accordingly, calibration component 322 may electronically adjust, modify, change, or otherwise shift the plurality of remaining parameter state instantiations 1002 according to or following the active setting adjustment 1202, thereby producing a plurality of modified remaining parameter state instantiations 1204.

[0146] As a non-limiting example, calibration component 322 may electronically adjust, modify, or change the numerical value of the remaining parameter state instantiation 1002(1) in any way, by any amount or percentage, or in any direction that would be expected to be caused by the active setting adjustment 1202. This may produce a modified remaining parameter state instantiation 1204(1). In other words, assuming that the true physical state of the scientific instrument 302 before the active setting adjustment 1202 matches the physical state indicated by the remaining parameter state instantiation 1204(1), the modified remaining parameter state instantiation 1002(1) may be considered to indicate any particular numerical value of the parameter state 308 that the scientific instrument 302 would be expected to have after the active setting adjustment 1202 is performed.

[0147] As another non - limiting example, the calibration component 322 can electronically adjust, modify, or change the numerical value of the remaining parameter state instantiation 1002(y) in any way, in any amount or percentage, or in any direction that would be expected to be caused by the active setting adjustment 1202. This can result in a modified remaining parameter state instantiation 1204(y). That is, assuming that the true physical state of the scientific instrument 302 before the active setting adjustment 1202 matches the physical state indicated by the remaining parameter state instantiation 1204(y), the modified remaining parameter state instantiation 1002(y) can be considered to indicate any particular numerical value of the parameter state 308 that the scientific instrument 302 would be expected to have after the active setting adjustment 1202 is performed.

[0148] It should be understood that in some embodiments, in addition to the active setting adjustment 1202, the scientific instrument 302 may also undergo passive time evolution (e.g., the internal temperature of the scientific instrument 302 may naturally or passively cool or warm over time). As described above, this passive time evolution can be considered to change the true physical state of the scientific instrument 302 in some known way, by some known amount or percentage, or in some known direction. In such embodiments, the calibration component 322 can take this passive time evolution into account in order to generate multiple modified remaining parameter state instantiations 1204. Note that in some cases, the passive time evolution of the scientific instrument 302 may be unobserved, unmeasured, or otherwise unknown. Even in such cases, when implemented as described herein, the full - state Bayesian filter 502 can be considered to be catching up with such unobserved passive time evolution.

[0149] In any case, the multiple modified remaining parameter state instantiations 1204 can be considered or treated as being exploitable to progressively constrain or limit the Bayesian evidence of the state space of the digital twin 306 during the (j + 1) - th calibration iteration (e.g., during a subsequent or later calibration iteration).

[0150] Specifically, and as described above, the calibration component 322 may select a plurality of sampled parameter state instantiations 802 according to a prior distribution of the state space during the j-th calibration iteration. In various aspects, the calibration component 322 may perform a recursive Bayesian update on the prior distribution (e.g., via Bayes' theorem) during the j-th calibration iteration by using a plurality of modified remaining parameter state instantiations 1204 as Bayesian evidence. This may result in a posterior distribution of the state space, where this posterior distribution is narrower or tighter than the prior distribution (hence the terms "constrained" or "restricted"). In fact, the posterior distribution may be shaped according to a plurality of modified remaining parameter state instantiations 1204. In various aspects, the posterior distribution calculated during the j-th calibration iteration may be treated as the prior distribution during the (j + 1)-th calibration iteration. Conversely, if j > 1, the prior distribution sampled during the j-th calibration iteration may be any posterior distribution calculated during the (j - 1)-th calibration iteration. If j = 1, the prior distribution sampled during the j-th calibration iteration may be any suitable initial prior distribution of the state space as needed (e.g., a uniform distribution or a Gaussian distribution).

[0151] Accordingly, the calibration component 322 may perform each of the plurality of calibration iterations 602 as described with respect to Figures 7 to 12 Eventually, there may be a calibration iteration during which the per-parameter variances will all satisfy a threshold variance value. Such a calibration iteration may be considered the final, end, or last calibration iteration among the plurality of calibration iterations 602 (e.g., may be considered calibration iteration 602(z)). During such an iteration, the calibration parameter state instantiation 504 may be calculated and may be considered a specific numerical value indicative of, estimating, or approximating the true physical state of the scientific instrument 302. In various aspects, the calibration component 322 may electronically instruct, command, or otherwise cause the parameter state 308 of the digital twin 306 to be assigned any specific numerical value indicated by the calibration parameter state instantiation 504.

[0152] Note that, as described with respect to Figures 6 to 12 the organization or structure of the full state Bayesian filter 502 may be considered a type of particle filter technique. However, this is merely a non-limiting example for ease of explanation and illustration. In various other cases, the full state Bayesian filter 502 may exhibit any other suitable organization or structure, such as a Kalman filter technique.

[0153] In various embodiments, in response to the parameter state 308 of the digital twin 306 being assigned a specific numerical value indicated by the calibration parameter state instantiation 504, the post-calibration component 324 may facilitate any suitable electronic action.

[0154] As a non - limiting example, the post - calibration component 324 can electronically generate any suitable electronic notification or alert that indicates that the digital twin 306 has been successfully calibrated or synchronized with the scientific instrument 302. In other words, this electronic notification or alert can be regarded as conveying that the digital twin 306 is ready or prepared to accurately, correctly, or reliably simulate, predict, or forecast the future performance or behavior of the scientific instrument 302. In some cases, the post - calibration component 324 can electronically transmit this electronic notification or alert to any suitable computing device. In other cases, the post - calibration component 324 can electronically present this electronic notification or alert on any suitable electronic display (e.g., computer screen, computer monitor, heads - up display, holographic display).

[0155] As another non - limiting example, the post - calibration component 324 can electronically instruct, command, or otherwise cause the digital twin 306 to simulate, predict, or forecast the performance or behavior of the scientific instrument 302 based on any suitable usage scenario indicated, identified, or selected by a user or technician of the scientific instrument 302. For example, the user or technician can use the human - machine interface device of the scientific instrument 302 to numerically define the proposed or desired usage scenario. In response to receiving such a numerical definition, the post - calibration component 324 can use the calibration parameter state instantiation 504 to cause the digital twin 306 to run the proposed or desired usage scenario. This can enable the digital twin 306 to accurately simulate, predict, or forecast how the scientific instrument 302 will actually behave or perform in the case where the scientific instrument operates according to the proposed or desired usage scenario.

[0156] Figures 13 to 15 Flowcharts exemplifying example non - limiting computer - implemented methods 1300, 1400, and 1500 that can facilitate improved digital twin calibration in accordance with one or more embodiments described herein. In various cases, the system 314 can facilitate the computer - implemented methods 1300, 1400, and 1500.

[0157] First, consider Figure 13 . In various embodiments, the action 1302 can include accessing a digital twin (e.g., 306) of a scientific instrument (e.g., 302) by a device operatively coupled to a processor (e.g., via 320).

[0158] In various aspects, the action 1304 can include initializing a prior parameter state distribution of the state space of the digital twin by the device (e.g., via 322). In some cases, the prior parameter state distribution can be initialized to a uniform distribution or a normal / Gaussian distribution of the state space.

[0159] In various cases, action 1306 may include the device (e.g., via 322) defining state-dependent observables (e.g., 604) that may be exhibited by the scientific instrument during operation and simulated by the digital twin. In some cases, the state-dependent observables may be associated with the standards, repeatable or baseline operation, usage scenarios, or samples of the scientific instrument.

[0160] In various aspects, action 1308 may include the device (e.g., via 322) operating the scientific instrument.

[0161] In various cases, action 1310 may include the device (e.g., via 322) measuring observations (e.g., 702) exhibited by the scientific instrument during operation of the scientific instrument and with respect to the state-dependent observables.

[0162] In various cases, action 1312 may include the device (e.g., via 322) randomly sampling multiple parameter state instantiations of the digital twin from a prior parameter state distribution (e.g., 802).

[0163] In various aspects, action 1314 may include the device (e.g., via 322) calculating multiple simulated observations (e.g., 804) with respect to the state-dependent observables and by separately running each of the multiple parameter state instantiations on the digital twin. In various cases, the computer-implemented method 1300 may proceed to action 1402 of the computer-implemented method 1400.

[0164] Now, consider Figure 14 . In various embodiments, action 1402 may include the device (e.g., via 322) assigning corresponding weights (e.g., 902) to the multiple parameter state instantiations based on the respective errors between the observations and the multiple simulated observations.

[0165] In various aspects, action 1404 may include the device (e.g., via 322) deleting any parameter state instantiations among the multiple parameter state instantiations that have weights below a threshold weight value. This may result in multiple remaining parameter state instantiations (e.g., 1002).

[0166] In various instantiations, action 1406 may include the device (e.g., via 322) calculating the per-parameter variance across the multiple remaining parameter state instantiations.

[0167] In various cases, action 1408 may include the device (e.g., via 322) determining whether all per-parameter variances satisfy (e.g., are below) a threshold variance value. If so, the computer-implemented method 1400 may proceed to action 1410. If not, the computer-implemented method 1400 may proceed to action 1502 of the computer-implemented method 1500.

[0168] In various aspects, action 1410 may include generating, by the device (e.g., via 324), an electronic notification that indicates that the digital twin and the scientific instrument are synchronized or calibrated at a weighted average (e.g., 504) of the instantiations of the plurality of remaining parameter states.

[0169] Now, consider Figure 15 . In various embodiments, action 1502 may include applying, by the device (e.g., via 322), an active adjustment (e.g., 1202) to one or more controllable settings (e.g., 304) of the scientific instrument.

[0170] In various aspects, action 1504 may include modifying, by the device (e.g., via 322), the instantiations of the plurality of remaining parameter states separately based on the active adjustment. In some cases, such modification may further be based on the passive temporal evolution of the scientific instrument. In any case, such modification may result in a plurality of modified instantiations of the remaining parameter states (e.g., 1204).

[0171] In various cases, action 1506 may include calculating, by the device (e.g., via 322), a posterior parameter state distribution by applying a recursive Bayesian update to a prior parameter state distribution based on the plurality of modified instantiations of the remaining parameter states (e.g., the plurality of modified instantiations of the remaining parameter states may be considered to provide Bayesian evidence in the application of Bayes' theorem).

[0172] In various cases, action 1508 may include redefining, by the device (e.g., via 322), the prior parameter state distribution to now be equal to the posterior parameter state distribution.

[0173] In various aspects, action 1510 may include returning, by the device (e.g., via 322), to action 1308 of the computer-implemented method 1300.

[0174] The inventors have experimentally verified the various embodiments described herein. Some results of such experimental verification are shown in Figures 16 to 22 .

[0175] In particular, the inventors have reduced to practice various embodiments in which: the scientific instrument 302 is a transmission electron microscope; the parameter state 308 is a two-parameter vector, where one parameter represents the microscope defocus aberration coefficient (e.g., measured in nanometers (nm)), and where the other parameter represents the microscope astigmatism aberration coefficient (e.g., measured in nm); and where the iterative common observable 604 refers to the Fourier transform of a CBED pattern applied to an amorphous carbon sample.

[0176] Figure 16Illustrated is the CBED pattern 1602 of an amorphous carbon sample. In experiments conducted by the present inventors, such a CBED pattern can actually be captured by a transmission electron microscope, but such a CBED pattern can also be synthesized or simulated by a digital twin of the transmission electron microscope. Figure 16 Further illustrated is the amplitude of the Fourier transform CBED pattern 1604. The Fourier transform CBED pattern 1604 is obtained by applying a Fourier transform to the CBED pattern 1602 and taking the amplitude of the resulting complex number.

[0177] Figure 17 Illustrated is a heat map 1700 demonstrating a real-world example of the parameter ambiguity problem. Specifically, the horizontal axis of the heat map 1700 represents at least a portion of the domain of the microscope defocus aberration coefficient parameter (e.g., the possible values that can be assigned). Additionally, the vertical axis of the heat map 1700 represents at least a portion of the domain of the microscope two-fold astigmatism aberration coefficient parameter (e.g., the possible values that can be assigned). These two domains together can be considered to define the state space of the digital twin of the transmission electron microscope.

[0178] Now, the present inventors cause the transmission electron microscope to capture the real CBED pattern of the amorphous carbon sample using some unknown real physical state, and a Fourier transform is applied to this real CBED pattern. Additionally, the present inventors cause the digital twin to be instantiated with approximately 400 unique, evenly spaced parameter states to span the state space of the digital twin. For each of these parameter state instantiations, the digital twin calculates the simulated CBED pattern of the amorphous carbon sample, and a Fourier transform is applied to each of such simulated CBED patterns.

[0179] Finally, the mean squared error between the Fourier transform version of the real CBED pattern and the Fourier transform version of each of such simulated CBED patterns is calculated. These mean squared errors are displayed in the heat map 1700, with darker colors representing lower mean squared errors and lighter colors representing higher mean squared errors. As shown by the dark regions of the heat map 1700, there are many possible parameter state instantiations that result in low mean squared errors (e.g., predicting simulated CBED patterns that are close to or similar to the real CBED pattern) for the generation of the digital twin. However, the real physical state of the transmission electron microscope only matches one of these many possible parameter state instantiations. Determining which one of these many possible parameter state instantiations is closest to the real physical state of the transmission electron microscope (e.g., calibrating the digital twin to the transmission electron microscope) is a difficult and non-trivial task. Despite this difficulty or non-triviality, the present inventors demonstrate that the various embodiments described herein can correctly, accurately, or reliably facilitate such determination.

[0180] In fact, the present inventor calibrates or synchronizes the aberration coefficient parameters of the digital twin to the true physical state of the transmission electron microscope by utilizing an embodiment of the full-state Bayesian filter 502.

[0181] Figure 18 A graph 1800 illustrating a first calibration iteration performed by this embodiment of the full-state Bayesian filter 502 is depicted. In graph 1800, the "+" represents the true data physical state of the transmission electron microscope estimated using the software platform. Additionally in FIG. 1800, each circle represents a corresponding one of the plurality of sampled parameter state instantiations 802, and the shading or color of each circle represents the weight corresponding to the corresponding one of the plurality of sampled parameter state instantiations 802 (e.g., one of 902). Specifically, a darker color represents a higher weight, while a lighter color represents a lower weight. As shown, the plurality of sampled parameter state instantiations 802 in the first calibration iteration exhibit a wide variance / variability (e.g., the plurality of sampled parameter state instantiations are dispersed from each other or not close).

[0182] Figure 19 A graph 1900 illustrating a second calibration iteration performed by this embodiment of the full-state Bayesian filter 502 is depicted. As shown in graph 1900, due to the active setting adjustment 1202 performed in the first calibration iteration, the true physical state of the transmission electron microscope has moved. Additionally as shown in graph 1900, the plurality of sampled parameter state instantiations 802 have moved closer to each other and are progressively more tightly packed around or near the true physical state.

[0183] Figure 20 A graph 2000 illustrating a third calibration iteration performed by this embodiment of the full-state Bayesian filter 502 is depicted. As shown in graph 2000, due to the active setting adjustment 1202 performed in the second calibration iteration, the true physical state of the transmission electron microscope has moved again. Additionally as shown in graph 2000, the plurality of sampled parameter state instantiations 802 have moved even closer to each other and are even more tightly packed around or near the true physical state.

[0184] Figure 21 A graph 2100 illustrating a fourth calibration iteration performed by this embodiment of the full-state Bayesian filter 502 is depicted. As shown in graph 2100, due to the active setting adjustment 1202 performed in the third calibration iteration, the true physical state of the transmission electron microscope has moved yet again. Additionally as shown in graph 2100, the plurality of sampled parameter state instantiations 802 have moved even closer to each other and are even more tightly packed around or near the true physical state.

[0185] Figure 22 A graph 2200 depicting a fifth calibration iteration performed by this implementation of the full state Bayesian filter 502. As shown in graph 2200, due to the active setting adjustment 1202 performed in the fourth calibration iteration, the true physical state of the transmission electron microscope has shifted once again. Also as shown in graph 2200, a plurality of sampled parameter state instantiations 802 are now extremely close to each other and close enough to the true physical state.

[0186] Thus, Figures 16 to 22 It can be regarded as showing how the various implementations described herein can accurately calibrate or synchronize the microscope aberration coefficient parameters of the digital twin with the true physical state of the transmission electron microscope, despite the parameter ambiguity problem. As explained throughout this disclosure, this accurate calibration or synchronization can be accomplished due to the following: the full state Bayesian filter 502 progressively updates all the parameters of the digital twin 306 at each calibration iteration, rather than updating different parameters in different iterations in the order of dependence; and the full state Bayesian filter 502 utilizes a single common observable (e.g., the Fourier-transformed CBED pattern) in all calibration iterations, rather than different observables in different iterations.

[0187] Although the various implementations described herein involve the iterative common observable 604 being the Fourier-transformed version of the CBED pattern depicting an amorphous carbon sample (e.g., a Ronchigram), these are merely non-limiting examples for ease of explanation and illustration. In various aspects, the iterative common observable 604 can take any other form or be expressed according to any other data representation. In fact, in some cases, the iterative common observable 604 can be a CBED pattern that has undergone some transformation other than the Fourier transform. Non-limiting examples of such other types of transformations can be any suitable feature extraction, such as edge, shift, power spectrum, or patching. In still other cases, the iterative common observable 604 can be a CBED pattern that has not undergone any transformation at all. In even other cases, the iterative common observable 604 can be the CBED pattern of any suitable non-carbon sample or non-amorphous sample.

[0188] Although the drawings herein show that the system 314 can be within or local to the scientific instrument 302, this is merely a non-limiting example for ease of explanation and illustration. In various other implementations, the system 314 can instead be remote from the scientific instrument 302. In fact, in some cases, the system 314 can be implemented on a dedicated computer that is designed or configured to control one or more scientific instruments, perform post-measurement analysis for one or more scientific instruments, or otherwise computationally support one or more scientific instruments.

[0189] Note that, in various aspects, the aberration coefficients of a charged particle microscope cannot be regarded as global characteristics of the charged particle microscope. In fact, the substantial content or meaning of the aberration coefficients of a charged particle microscope can vary with different positions along the optical axis of the charged particle microscope. As a non-limiting example, if the beam of the charged particle microscope is focused on a sample, the aberration coefficients can be defined in the condenser aperture plane of the charged particle microscope. In this case, the aberration coefficients can be referred to as the probe aberrations of the charged particle microscope. As another non-limiting example, if the beam of the charged particle microscope is made parallel to the sample, the aberration coefficients can be defined in the sample plane of the charged particle microscope. In this case, the aberration coefficients can be referred to as the image aberrations. Thus, as described herein, the term "aberration coefficients" can be regarded as a superordinate or umbrella term encompassing or covering any suitable type of aberration defined with respect to any suitable position along the optical axis of the charged particle microscope.

[0190] Note that, in some aspects, the system 314 can be implemented, activated, or otherwise invoked periodically, continuously, or continuously so as to more frequently synchronize the parameter state 308 of the digital twin 306 with the physical state of the scientific instrument 302 or otherwise calibrate it. However, in other aspects, the system 314 can instead be implemented, activated, or otherwise invoked in an aperiodic or ad hoc manner so as to less frequently synchronize the parameter state 308 of the digital twin 306 with the physical state of the scientific instrument 302 or otherwise calibrate it.

[0191] The scientific instrument systems, methods, or technologies disclosed herein can include (e.g., via the user local computing device 2520 discussed herein with reference to Figure 25 interactions with a human user. These interactions can include providing information to the user (e.g., information about the operation of a scientific instrument (such as Figure 25 scientific instrument 2510), information about the sample being analyzed or other tests or measurements performed by the scientific instrument, information retrieved from a local or remote database, or other information) or providing the option to input commands to the user (e.g., controlling the operation of a scientific instrument (such as Figure 25 scientific instrument 2510), or controlling the analysis of data generated by the scientific instrument), queries (e.g., queries to a local or remote database), or other information. In some embodiments, these interactions can be performed via a graphical user interface (GUI) that includes a visual display on a display device (e.g., the display device 2410 discussed herein with reference to Figure 24 which provides output to the user and / or prompts the user (e.g., via the reference herein to Figure 24One or more input devices included in the other I / O devices 2412 being discussed, such as a keyboard, mouse, trackpad, or touchscreen, provide input. The scientific instrument systems, methods, or technologies disclosed herein may include any suitable GUI for interacting with a user.

[0192] Figure 23 Depicted is an example graphical user interface 2300 (hereinafter referred to as "GUI 2300") that can be used to perform some or all of the support methods or technologies disclosed herein according to various embodiments. In various aspects, the GUI 2300 can be provided on any suitable electronic display of a computing device (e.g., the computing device 2400 discussed herein with reference to Figure 25 the scientific instrument support system 2500 being discussed herein), and a user or technician can interact with the GUI 2300 using any suitable input device (e.g., any one of the other I / O devices 2412 discussed herein with reference to Figure 24 the computing device 2400 being discussed herein) and input technologies (e.g., cursor movement, motion capture, face recognition, gesture detection, speech recognition, button activation). Figure 24 the display device 2410 being discussed herein), and a user or technician can interact with the GUI 2300 using any suitable input device (e.g., any one of the other I / O devices 2412 discussed herein with reference to Figure 24 the other I / O devices 2412 being discussed herein) and input technologies (e.g., cursor movement, motion capture, face recognition, gesture detection, speech recognition, button activation).

[0193] The GUI 2300 can include a data display area 2302, a data analysis area 2304, a scientific instrument control area 2306, and a settings area 2308. Figure 23 The specific number and arrangement of the depicted areas are merely illustrative, and any number and arrangement of areas (including any desired features) can be included in other embodiments of the GUI 2300.

[0194] The data display area 2302 can display data generated by a scientific instrument (e.g., the scientific instrument 2510 discussed herein with reference to Figure 25 the scientific instrument 2510 being discussed herein).

[0195] The data analysis area 2304 can display any suitable data analysis results (e.g., the results of analyzing the data illustrated in the data display area 2302 or other data). In some embodiments, the data display area 2302 and the data analysis area 2304 can be combined in the GUI 2300 (e.g., including the data output from a scientific instrument and some analysis of the data in a common graph or area).

[0196] The scientific instrument control area 2306 can include options that allow a user or technician to control the scientific instrument (e.g., the scientific instrument 2510 discussed herein with reference to Figure 25The scientific instrument under discussion (2510). For example, the scientific instrument control area 2306 may include configurable parameters for controlling the operation of such a scientific instrument (e.g., configurable parameters for controlling the voltage or current of the scientific instrument, configurable parameters for controlling the internal temperature of the scientific instrument, or configurable parameters for controlling the fluid flow rate of the scientific instrument).

[0197] The settings area 2308 may include options that allow a user or technician to control any feature or function of the GUI 2300 (or other GUI) or perform common computational operations on the data display area 2302 and the data analysis area 2304 (e.g., saving data on a storage device (such as the storage device 2404 discussed herein with reference to Figure 24 the storage device discussed), sending the data to another user, marking the data).

[0198] As described above, the scientific instrument module 102 may be implemented by one or more computing devices. Figure 24 is a block diagram of a computing device 2400 that can execute some or all of the scientific instrument support methods or techniques disclosed herein according to various embodiments. In some embodiments, the scientific instrument module 102 may be implemented by a single instance of the computing device 2400 or multiple instances of the computing device 2400. Additionally, as discussed below, the computing device 2400 (or multiple instances of the computing device) that implements the scientific instrument module 102 may be Figure 25 a part of one or more of the scientific instrument 2510, the user local computing device 2520, the service local computing device 2530, or the remote computing device 2540.

[0199] The computing device 2400 is illustrated as having multiple components, but any one or more of these components may be omitted or replicated depending on the application and settings. In some embodiments, some or all of the components included in the computing device 2400 may be attached to one or more motherboards and encapsulated in a housing (e.g., including plastic, metal, or other materials). In some embodiments, some of these components may be fabricated onto a single system-on-chip (SoC) (e.g., the SoC may include one or more instances of the processing device 2402 and one or more instances of the storage device 2404). Additionally, in various embodiments, the computing device 2400 may omit Figure 24One or more of the illustrated components, but may include interface circuitry (not shown) for coupling to one or more omitted components using any suitable interface (e.g., Universal Serial Bus (USB) interface, High-Definition Multimedia Interface (HDMI) interface, Controller Area Network (CAN) interface, Serial Peripheral Interface (SPI) interface, Ethernet interface, wireless interface, or any other suitable interface). For example, computing device 2400 may omit display device 2410, but may include display device interface circuitry (e.g., connectors and driver circuitry) to which display device 2410 may couple.

[0200] Computing device 2400 may include processing device 2402 (e.g., one or more processing devices). As used herein, the term "processing device" may refer to any device or portion of a device that processes electronic data from registers or memory to transform that electronic data into other electronic data that may be stored in registers or memory. Processing device 2402 may include one or more digital signal processors (DSPs), application specific integrated circuits (ASICs), central processing units (CPUs), graphics processing units (GPUs), cryptographic processors (specialized processors that execute cryptographic algorithms in hardware), server processors, or any other suitable processing device.

[0201] Computing device 2400 may include storage device 2404 (e.g., one or more storage devices). Storage device 2404 may include one or more memory devices, such as random access memory (RAM) (e.g., static RAM (SRAM) devices, magnetic RAM (MRAM) devices, dynamic RAM (DRAM) devices, resistive RAM (RRAM) devices, or conductive-bridging RAM (CBRAM) devices), hard-drive-based memory devices, solid-state memory devices, network drives, cloud drives, or any combination of memory devices. In some embodiments, storage device 2404 may include memory that shares a die with processing device 2402. In such embodiments, the memory may be used as a cache memory and may include, for example, embedded dynamic random access memory (eDRAM) or spin-transfer torque magnetic random access memory (STT-MRAM). In some embodiments, storage device 2404 may include a non-transitory computer-readable medium having instructions thereon that, when executed by one or more processing devices (e.g., processing device 2402), cause computing device 2400 to perform any suitable method or portions of the methods disclosed herein.

[0202] The computing device 2400 may include interface devices 2406 (e.g., one or more instances of interface device 2406). The interface device 2406 may include one or more communication chips, connectors, or other hardware and software to manage communication between the computing device 2400 and other computing devices. For example, the interface device 2406 may include circuitry for managing wireless communication that is used to transfer data to and from the computing device 2400. The term "wireless" and its derivatives may be used to describe circuits, devices, systems, methods, techniques, communication channels that may transfer data by using modulated electromagnetic radiation through a non-solid medium. The term does not imply that the associated devices do not contain any wires, although in some embodiments they may not contain any wires. The circuitry for managing wireless communication included in the interface device 2406 may implement any one of a variety of wireless standards or protocols, including but not limited to Institute of Electrical and Electronics Engineers (IEEE) standards, including Wi-Fi (IEEE 802.11 series), IEEE 802.16 standards (e.g., IEEE 802.16-2005 amendment), Long Term Evolution (LTE) project, and any amendments, updates, and / or revisions (e.g., LTE-Advanced project, Ultra Mobile Broadband (UMB) project (also known as "3GPP2")). In some embodiments, the circuitry for managing wireless communication included in the interface device 2406 may operate in accordance with Global System for Mobile Communications (GSM), General Packet Radio Service (GPRS), Universal Mobile Telecommunications System (UMTS), High Speed Packet Access (HSPA), Evolved HSPA (E-HSPA), or LTE networks. In some embodiments, the circuitry for managing wireless communication included in the interface device 2406 may operate in accordance with Enhanced Data GSM Evolution (EDGE), GSM EDGE Radio Access Network (GERAN), Universal Terrestrial Radio Access Network (UTRAN), or Evolved UTRAN (E-UTRAN). In some embodiments, the circuitry for managing wireless communication included in the interface device 2406 may operate in accordance with Code Division Multiple Access (CDMA), Time Division Multiple Access (TDMA), Digital Enhanced Cordless Telecommunications (DECT), Evolution-Data Optimized (EV-DO), and their derivatives, and any other wireless protocols designated as 3G, 4G, 5G, and later. In some embodiments, the interface device 2406 may include one or more antennas (e.g., one or more antenna arrays) to receive and / or transmit wireless communication.

[0203] In some embodiments, interface device 2406 may include circuitry for managing wired communications, such as electrical communication protocols, optical communication protocols, or any other suitable communication protocol. For example, interface device 2406 may include circuitry that supports communication according to Ethernet technology. In some embodiments, interface device 2406 may support both wireless and wired communications, or may support multiple wired communication protocols or multiple wireless communication protocols. For example, a first set of circuitry of interface device 2406 may be dedicated to short-range wireless communications such as Wi-Fi or Bluetooth, while a second set of circuitry of interface device 2406 may be dedicated to long-range wireless communications such as Global Positioning System (GPS), EDGE, GPRS, CDMA, WiMAX, LTE, EV-DO, etc. In some embodiments, a first set of circuitry of interface device 2406 may be dedicated to wireless communications, while a second set of circuitry of interface device 2406 may be dedicated to wired communications.

[0204] Computing device 2400 may include battery / power circuitry 2408. Battery / power circuitry 2408 may include one or more energy storage devices (e.g., batteries or capacitors) or circuitry for coupling components of computing device 2400 to an energy source (e.g., alternating current line power) separate from computing device 2400.

[0205] Computing device 2400 may include a display device 2410 (e.g., multiple display devices). Display device 2410 may include any visual indicator, such as a heads-up display, a computer monitor, a projector, a touchscreen display, a liquid crystal display (LCD), a light-emitting diode display, or a flat panel display.

[0206] Computing device 2400 may include other input / output (I / O) devices 2412. For example, other I / O devices 2412 may include one or more audio output devices (e.g., speakers, headphones, earbuds, sirens), one or more audio input devices (e.g., microphones or microphone arrays), positioning devices (e.g., GPS devices that communicate with satellite-based systems to receive the orientation of computing device 2400), audio codecs, video codecs, printers, sensors (e.g., thermocouples or other temperature sensors, humidity sensors, pressure sensors, vibration sensors, accelerometers, gyroscopes), image capture devices such as cameras, keyboards, cursor control devices such as mice, styli, trackballs, or touchpads, barcode readers, quick response (QR) code readers, or radio frequency identification (RFID) readers.

[0207] The computing device 2400 can have any suitable form factor for its applications and settings, such as a handheld or mobile computing device (e.g., a phone, smartphone, mobile Internet device, tablet computer, laptop computer, netbook computer, ultrabook computer, personal digital assistant (PDA), ultra-mobile personal computer), a desktop computing device, or a server computing device or other networked computing components.

[0208] One or more computing devices implementing any of the scientific instrument modules, methods, or techniques disclosed herein can be part of a scientific instrument support system. Figure 25 FIG. 2500 is a block diagram of an example scientific instrument support system 2500 in which some or all of the scientific instrument support methods disclosed herein can be performed according to various embodiments. The scientific instrument modules, methods, or techniques disclosed herein (e.g., scientific instrument module 102, computer-implemented method 200, system 314, computer-implemented methods 1300-1500) can be implemented by one or more of the scientific instruments 2510, user local computing devices 2520, service local computing devices 2530, or remote computing devices 2540 of the scientific instrument support system 2500.

[0209] Any of the scientific instruments 2510, user local computing devices 2520, service local computing devices 2530, or remote computing devices 2540 can include any embodiment of the computing device 2400, and any of the scientific instruments 2510, user local computing devices 2520, service local computing devices 2530, or remote computing devices 2540 can take the form of any suitable embodiment of an embodiment of the computing device 2400.

[0210] The scientific instrument 2510, the user local computing device 2520, the service local computing device 2530, or the remote computing device 2540 may each include a processing device 2502, a storage device 2504, and an interface device 2506. The processing device 2502 may be in any suitable form, including any form of the processing device 2402, and the processing devices 2502 included in different devices among the scientific instrument 2510, the user local computing device 2520, the service local computing device 2530, or the remote computing device 2540 may be in the same form or different forms. The storage device 2504 may be in any suitable form, including any form of the storage device 2404, and the storage devices 2504 included in different devices among the scientific instrument 2510, the user local computing device 2520, the service local computing device 2530, or the remote computing device 2540 may be in the same form or different forms. The interface device 2506 may be in any suitable form, including any form of the interface device 2406, and the interface devices 2506 included in different devices among the scientific instrument 2510, the user local computing device 2520, the service local computing device 2530, or the remote computing device 2540 may be in the same form or different forms.

[0211] The scientific instrument 2510, the user local computing device 2520, the service local computing device 2530, and the remote computing device 2540 may communicate with other elements of the scientific instrument support system 2500 via a communication path 2508. The communication path 2508 may communicatively couple the interface devices 2506 of different elements among the elements of the scientific instrument support system 2500, as shown, and may be a wired or wireless communication path (e.g., according to any one of the communication technologies discussed herein with reference to the interface device 2406). Figure 25 The depicted specific scientific instrument support system 2500 includes communication paths between each pair of devices among the scientific instrument 2510, the user local computing device 2520, the service local computing device 2530, and the remote computing device 2540, but this specific implementation of "fully connected" is merely illustrative, and in various embodiments, various communication paths in the communication path 2508 may not exist. For example, in some embodiments, the service local computing device 2530 may lack a direct communication path 2508 between its interface device 2506 and the interface device 2506 of the scientific instrument 2510, but may instead communicate with the scientific instrument 2510 via the communication path 2508 between the service local computing device 2530 and the user local computing device 2520 and the communication path 2508 between the user local computing device 2520 and the scientific instrument 2510.

[0212] The scientific instrument 2510 may include any suitable scientific instrument, such as the scientific instrument 302.

[0213] The user local computing device 2520 can be a computing device local to the user of the scientific instrument 2510 (e.g., according to any of the embodiments of the computing device 2400). In some embodiments, the user local computing device 2520 can also be local to the scientific instrument 2510, but this is not necessarily the case; for example, a user local computing device 2520 in a user's home or office can be remote from the scientific instrument 2510 but communicate with the scientific instrument such that the user can use the user local computing device 2520 to control or access data from the scientific instrument 2510. In some embodiments, the user local computing device 2520 can be a laptop computer, a smart phone, or a tablet device. In some embodiments, the user local computing device 2520 can be a portable computing device.

[0214] The service local computing device 2530 can be a computing device local to an entity that services the scientific instrument 2510 (e.g., according to any of the embodiments of the computing device 2400). For example, the service local computing device 2530 can be a local device of the manufacturer of the scientific instrument 2510 or a third-party service company. In some embodiments, the service local computing device 2530 can communicate (e.g., via the direct communication path 2508 or via multiple "indirect" communication paths 2508, as discussed above) with the scientific instrument 2510, the user local computing device 2520, or the remote computing device 2540 to receive data regarding the operation of the scientific instrument 2510, the user local computing device 2520, or the remote computing device 2540 (e.g., self-test results of the scientific instrument 2510, calibration coefficients used by the scientific instrument 2510, measurements of sensors associated with the scientific instrument 2510). In some embodiments, the service local computing device 2530 can communicate (e.g., via the direct communication path 2508 or via multiple "indirect" communication paths 2508, as discussed above) with the scientific instrument 2510, the user local computing device 2520, or the remote computing device 2540 to transmit data to the scientific instrument 2510, the user local computing device 2520, or the remote computing device 2540 (e.g., update programming instructions (such as firmware) in the scientific instrument 2510, initiate the execution of a test or calibration sequence in the scientific instrument 2510, update programming instructions (such as software) in the user local computing device 2520 or the remote computing device 2540). A user of the scientific instrument 2510 can utilize the scientific instrument 2510 or the user local computing device 2520 to communicate with the service local computing device 2530 to report problems with the scientific instrument 2510 or the user local computing device 2520, request a technician visit to improve the operation of the scientific instrument 2510, order consumables or replacement parts associated with the scientific instrument 2510, or for other purposes.

[0215] The remote computing device 2540 can be a computing device that is remote from the scientific instrument 2510 or the user local computing device 2520 (e.g., according to any of the embodiments of the computing device 2400 discussed herein). In some embodiments, the remote computing device 2540 can be included in a data center or other large-scale server environment. In some embodiments, the remote computing device 2540 can include a network-attached storage device (e.g., as part of the storage device 2504). The remote computing device 2540 can store data generated by the scientific instrument 2510, perform an analysis of the data generated by the scientific instrument 2510 (e.g., according to programming instructions), facilitate communication between the user local computing device 2520 and the scientific instrument 2510, or facilitate communication between the service local computing device 2530 and the scientific instrument 2510.

[0216] In some embodiments, one or more of the elements of the Figure 25 illustrated scientific instrument support system 2500 can be omitted. Additionally, in some embodiments, Figure 25 multiple of the various elements of the scientific instrument support system 2500 can be present. For example, the scientific instrument support system 2500 can include multiple user local computing devices 2520 (e.g., different user local computing devices 2520 associated with different users or located in different orientations). As another example, the scientific instrument support system 2500 can include multiple scientific instruments 2510, all of which communicate with the service local computing device 2530 and / or the remote computing device 2540; in such an embodiment, the service local computing device 2530 can monitor the multiple scientific instruments 2510, and the service local computing device 2530 can cause updates or other information to be "broadcast" to the multiple scientific instruments 2510 simultaneously. The different scientific instruments 2510 in the scientific instrument support system 2500 can be close to each other (e.g., in the same room) or far from each other (e.g., on different floors of a building, in different buildings, in different cities, etc.). In some embodiments, the scientific instrument 2510 can be connected to an Internet of Things (IoT) stack that allows the scientific instrument 2510 to be commanded and controlled via a web-based application, a virtual or augmented reality application, a mobile application, or a desktop application. Any of these applications can be accessed by a user operating the user local computing device 2520, which communicates with the scientific instrument 2510 via an intermediate remote computing device 2540. In some embodiments, the scientific instrument 2510 can be sold by a manufacturer together with one or more associated user local computing devices 2520 as part of the local scientific instrument computing unit 2512.

[0217] In some embodiments, the different scientific instruments 2510 included in the scientific instrument support system 2500 can be different types of scientific instruments 2510; for example, one scientific instrument 2510 can be a mass spectrometer, while another scientific instrument 2510 can be a chromatograph or an autosampler. In some such embodiments, the remote computing device 2540 or the user's local computing device 2520 can combine data from the different types of scientific instruments 2510 included in the scientific instrument support system 2500.

[0218] In various cases, machine learning algorithms or models can be implemented in any suitable way to facilitate any suitable aspect described herein. To facilitate some of the machine learning aspects among the above-described machine learning aspects of the various embodiments, consider the following discussion of artificial intelligence (AI). The various embodiments described herein can employ artificial intelligence to facilitate automating one or more features or functions. These components can adopt various AI-based solutions to perform the various embodiments / examples disclosed herein. To provide or assist with the numerous determinations (e.g., determine, ascertain, infer, compute, predict, prognose, estimate, derive, forecast, detect, calculate) described herein, the components described herein can examine all or a subset of the data to which they are granted access and can provide reasoning or determination of the state of a system or environment from a set of observations such as captured via events or data. For example, determinations can be used to identify a particular context or action, or a probability distribution of a state can be generated. These determinations can be probabilistic; that is, a probability distribution of a state of interest is calculated based on consideration of data and events. Determination can also refer to techniques employed to compose higher-level events from a set of events or data.

[0219] Such determinations can result in the construction of new events or actions from a set of observed events or stored event data, regardless of whether the events are temporally close and regardless of whether the events and data are from one or several events and data sources. The components disclosed herein can adopt various classification (explicit training (e.g., via training data) and implicit training (e.g., via observed behavior, preferences, historical information, receiving external information, etc.)) schemes or systems (e.g., support vector machines, neural networks, expert systems, Bayesian belief networks, fuzzy logic, data fusion engines, etc.) in connection with performing automated or determined actions related to the claimed subject matter. Thus, classification schemes or systems can be used to automatically learn and perform multiple functions, actions, or determinations.

[0220] The classifier can take an input attribute vector z = (z1, z2, z3, z4, z n)Maps to the confidence that the input belongs to a certain class, such as f(z) = confidence(class). This classification can be determined using probability or statistics-based analysis (e.g., considering analysis utility and cost) to determine the actions that will be automatically performed. Support Vector Machines (SVMs) can be used as an example of a classifier that can be employed. SVMs operate by finding a hyperplane in the space of possible inputs, where the hyperplane attempts to separate triggering criteria from non-triggering events. Intuitively, this makes the classification correct for test data that is close to but not the same as the training data. Other directed and non-directed model classification methods include, for example, Naive Bayes, Bayesian networks, decision trees, neural networks, fuzzy logic models, or probability classification models that provide different independent models, and any one of them can be adopted. The classification used herein also includes statistical regression for developing priority models.

[0221] To provide additional context for the various embodiments described herein, Figure 26 and the following discussion is intended to provide a brief, general description of a suitable computing environment 2600 in which the embodiments described herein can be implemented. While the embodiments have been described above in the general context of computer-executable instructions that can run on one or more computers, those skilled in the art will recognize that the embodiments can also be implemented in combination with other program modules or as a combination of hardware and software.

[0222] Generally, program modules include routines, programs, components, data structures, etc. that perform specific tasks or implement specific abstract data types. In addition, those skilled in the art will understand that the methods of the present invention can be practiced with other computer system configurations, including single-processor or multi-processor computer systems, minicomputers, mainframe computers, Internet of Things (IoT) devices, distributed computing systems, and personal computers, handheld computing devices, microprocessor-based or programmable consumer electronics, etc., each of which is operatively coupled to one or more associated devices.

[0223] The embodiments shown herein can also be practiced in a distributed computing environment, where certain tasks are performed by remote processing devices linked by a communication network. In a distributed computing environment, program modules can be located in both local and remote memory storage devices.

[0224] Computing devices generally include various media, which may include computer-readable storage media, machine-readable storage media, or communication media, and the use of these two terms in this article is different from each other as follows. Computer-readable storage media or machine-readable storage media can be any available storage media accessible by a computer, and include volatile and non-volatile media, removable and non-removable media. By way of example and not limitation, computer-readable storage media or machine-readable storage media can be implemented in conjunction with any method or technology for storing information (such as computer-readable or machine-readable instructions, program modules, structured data, or unstructured data).

[0225] Computer-readable storage media may include, but are not limited to, random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD ROM), digital versatile disc (DVD), Blu-ray disc (BD) or other optical disc storage devices, magnetic tape cartridges, tapes, magnetic disk storage devices or other magnetic storage devices, solid state drives or other solid state storage devices, or other tangible or non-transitory media that can be used to store the desired information. In this regard, the terms "tangible" or "non-transitory" as applied to storage devices, memory, or computer-readable media in this article should be understood to exclude only propagating transitory signals themselves as a modifier, and do not waive the rights to all standard storage devices, memory, or computer-readable media that are more than just propagating transitory signals themselves.

[0226] Computer-readable storage media can be accessed by one or more local or remote computing devices, for example, via access requests, queries, or other data retrieval protocols, for various operations regarding the information stored by the media.

[0227] Communication media typically contain computer-readable instructions, data structures, program modules, or other structured or unstructured data in data signals (such as modulated data signals, for example, carrier waves or other transmission mechanisms), and include any information delivery or transmission medium. The term "modulated data signal" or signal refers to a signal whose one or more characteristics are set or changed to encode information in one or more signals. By way of example and not limitation, communication media include wired media (such as wired networks or direct wired connections) and wireless media (such as acoustic, RF, infrared, and other wireless media).

[0228] Refer again to Figure 26, An example environment 2600 for various embodiments for implementing the aspects described herein includes a computer 2602 that includes a processing unit 2604, a system memory 2606, and a system bus 2608. The system bus 2608 couples system components including, but not limited to, the system memory 2606 to the processing unit 2604. The processing unit 2604 can be any of a variety of commercially available processors. Dual microprocessors and other multi-processor architectures can also be used as the processing unit 2604.

[0229] The system bus 2608 can be any of several types of bus structures and can further use any of a variety of commercially available bus architectures to interconnect with a memory bus (with or without a memory controller), a peripheral bus, and a local bus. The system memory 2606 includes a ROM 2610 and a RAM 2612. A basic input / output system (BIOS) can be stored in non-volatile memory (such as ROM, erasable programmable read-only memory (EPROM), EEPROM), where the BIOS contains basic routines that help transfer information between elements within the computer 2602 during startup. The RAM 2612 can also include high-speed RAM, such as static RAM for caching data.

[0230] The computer 2602 also includes an internal hard disk drive (HDD) 2614 (e.g., EIDE, SATA), one or more external storage devices 2616 (e.g., a magnetic floppy disk drive (FDD) 2616, a memory stick or flash drive reader, a memory card reader, etc.), and a drive 2620, such as, for example, a solid-state drive, an optical disc drive, which can read from or write to a disc 2622 (such as a CD-ROM disc, a DVD, a BD, etc.). Alternatively, in the case of a solid-state drive, unless it is separate, the disc 2622 will not be included. Although the internal HDD 2614 is illustrated as being within the computer 2602, the internal HDD 2614 can also be configured for external use in a suitable chassis (not shown). Additionally, although not shown in the environment 2600, a solid-state drive (SSD) can be used to supplement or replace the HDD 2614. The HDD 2614, the external storage device 2616, and the drive 2620 can be connected to the system bus 2608 through an HDD interface 2624, an external storage interface 2626, and a drive interface 2628, respectively. The interface 2624 for the specific implementation of the external drive can include at least one or both of a universal serial bus (USB) and an Institute of Electrical and Electronics Engineers (IEEE) 1394 interface technology. Other external drive connection technologies are also within the scope of the embodiments described herein.

[0231] The drive and its associated computer-readable storage medium provide non-volatile storage of data, data structures, computer-executable instructions, and the like. For computer 2602, the drive and storage medium accommodate the storage of any data in a suitable digital format. Although the above description of computer-readable storage media refers to corresponding types of storage devices, those skilled in the art should understand that other types of storage media readable by a computer (whether currently existing or to be developed in the future) may also be used in the exemplary operating environment, and further, any such storage media may contain computer-executable instructions for performing the methods described herein.

[0232] Multiple program modules may be stored in the drive and in RAM 2612, including an operating system 2630, one or more application programs 2632, other program modules 2634, and program data 2636. All or part of the operating system, applications, modules, or data may also be cached in RAM 2612. The systems and methods described herein may be implemented using a variety of commercially available operating systems or combinations of operating systems.

[0233] Computer 2602 may optionally include emulation technology. For example, a hypervisor (not shown) or other intermediary may emulate the hardware environment for operating system 2630, and the emulated hardware may optionally be different from Figure 26 the hardware illustrated. In such embodiments, operating system 2630 may include one VM among multiple virtual machines (VMs) hosted at computer 2602. Additionally, operating system 2630 may provide a runtime environment for application programs 2632, such as a Java runtime environment or a.NET framework. A runtime environment is a consistent execution environment that allows application programs 2632 to run on any operating system that includes the runtime environment. Similarly, operating system 2630 may support containers, and application programs 2632 may be in the form of containers, which are lightweight, independent, executable software packages that include, for example, the code of the application program, runtime, system tools, system libraries, and settings.

[0234] Furthermore, computer 2602 may be equipped with a security module, such as a Trusted Platform Module (TPM). For example, using the TPM, the boot component hashes the next boot component over time and waits for the result to match a security value before loading the next boot component. This process may occur at any layer in the code execution stack of computer 2602, e.g., applied at the application execution level or the operating system (OS) kernel level, thus enabling security for any level of code execution.

[0235] A user may input commands and information into the computer 2602 through one or more wired / wireless input devices (e.g., keyboard 2638, touch screen 2640, and pointing devices such as mouse 2642). Other input devices (not shown) may include a microphone, an infrared (IR) remote control, a radio frequency (RF) remote control or other remote controls, a joystick, a virtual reality controller or virtual reality headset, a gamepad, a stylus, an image input device (e.g., a camera), a gesture sensor input device, a visual motion sensor input device, an emotion or face detection device, a biometric input device (e.g., a fingerprint or iris scanner), etc. These and other input devices are typically connected to the processing unit 2604 through an input device interface 2644 that can be coupled to the system bus 2608, but may also be connected through other interfaces, such as a parallel port, an IEEE 1394 serial port, a game port, a USB port, an IR interface, interfaces, etc.

[0236] A monitor 2646 or other type of display device may also be connected to the system bus 2608 via an interface (such as a video adapter 2648). In addition to the monitor 2646, a computer typically also includes other peripheral output devices (not shown), such as speakers, printers, etc.

[0237] The computer 2602 may operate in a networking environment using a logical connection to one or more remote computers (such as remote computer 2650) via wired or wireless communication. The remote computer 2650 may be a workstation, a server computer, a router, a personal computer, a portable computer, a microprocessor-based entertainment device, a peer device, or other common network nodes, and typically includes many or all of the elements described with respect to the computer 2602, but only the memory / storage device 2652 is illustrated for brevity. The depicted logical connections include a wired / wireless connection to a local area network (LAN) 2654 or a larger network (e.g., a wide area network (WAN) 2656). Such LAN and WAN networking environments are common in offices and companies and facilitate the establishment of enterprise-wide computer networks (e.g., intranets), all of which can be connected to a global communication network (e.g., the Internet).

[0238] When used in a LAN networking environment, the computer 2602 may be connected to the local network 2654 through a wired or wireless communication network interface or adapter 2658. The adapter 2658 may facilitate wired or wireless communication with the LAN 2654, which may also include a wireless access point (AP) disposed thereon for communicating with the adapter 2658 in wireless mode.

[0239] When used in a WAN networking environment, computer 2602 may include a modem 2660 or may be connected to a communication server on WAN 2656 via other components for establishing communications over WAN 2656 (such as over the Internet). Modem 2660 may be connected to system bus 2608 via input device interface 2644, and the modem may be internal or external to a wired or wireless device and may be a wired or wireless device. In a networking environment, program modules depicted relative to computer 2602 or portions thereof may be stored in remote memory / storage device 2652. It should be appreciated that the network connections shown are examples and other components may be used to establish a communications link between computers.

[0240] When used in a LAN or WAN networking environment, computer 2602 may access a cloud storage system or other network-based storage system in addition to or instead of external storage device 2616 as described above, such as but not limited to network virtual machines that provide one or more aspects of storage or processing of information. Generally, the connection between computer 2602 and the cloud storage system may be established, for example, by adapter 2658 or modem 2660 over LAN 2654 or WAN 2656, respectively. When connecting computer 2602 to an associated cloud storage system, external storage interface 2626 may manage the storage provided by the cloud storage system with the help of adapter 2658 or modem 2660, just as it manages other types of external storage. For example, external storage interface 2626 may be configured to provide access to cloud storage sources as if these sources were physically connected to computer 2602.

[0241] Computer 2602 is operable to communicate with any wireless device or entity operating in a wireless communication manner (e.g., printers, scanners, desktop or portable computers, portable data assistants, communication satellites, any equipment or location associated with a wirelessly detectable tag (e.g., kiosks, newsstands, store shelves, etc.) and telephones). This may include Wi-Fi and wireless technologies. Thus, the communication may be a predefined structure like a traditional network or may be an ad hoc communication between at least two devices.

[0242] Figure 27is a schematic block diagram of an example computing environment 2700 with which the disclosed subject matter may interact. The example computing environment 2700 includes one or more clients 2710. The clients 2710 can be hardware or software (e.g., threads, processes, computing devices). The example computing environment 2700 also includes one or more servers 2730. The servers 2730 can also be hardware or software (e.g., threads, processes, computing devices). For example, the server 2730 can host threads to perform transformations by adopting one or more embodiments described herein. A possible communication between the clients 2710 and the servers 2730 can take the form of data packets suitable for transfer between two or more computer processes. The example computing environment 2700 includes a communication framework 2750 that can be used to facilitate communication between the clients 2710 and the servers 2730. The clients 2710 are operatively connected to one or more client data repositories 2720 that can be used to store information local to the clients 2710. Similarly, the servers 2730 are operatively connected to one or more server data repositories 2740 that can be used to store information local to the servers 2730.

[0243] Various embodiments can be a system, a method, an apparatus, or a computer program product at any possible technical detail integration level. The computer program product can include a computer-readable storage medium (or media) having computer-readable program instructions thereon for causing a processor to perform aspects of various embodiments. The computer-readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer-readable storage medium can be, for example, but not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of the computer-readable storage medium can also include the following: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disc (DVD), a memory stick, a floppy disk, a mechanically encoded device (such as a punched card or raised structures in grooves having instructions recorded thereon), and any suitable combination of the foregoing. As used herein, a computer-readable storage medium should not be construed as a transient signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., an optical pulse transmitted through an optical fiber cable), or an electrical signal transmitted through a wire.

[0244] The computer-readable program instructions described herein can be downloaded to a corresponding computing / processing device from a computer-readable storage medium or downloaded to an external computer or an external storage device via a network (e.g., the Internet, a local area network, a wide area network, or a wireless network). The network can include copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers, or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium within the corresponding computing / processing device. The computer-readable program instructions for performing the operations of the various implementations can be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state-setting data, configuration data for integrated circuits, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages (e.g., Smalltalk, C++, etc.) and procedural programming languages (e.g., the "C" programming language or similar programming languages). The computer-readable program instructions can be executed entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer, or entirely on the remote computer or server. In the latter case, the remote computer can be connected to the user's computer through any type of network connection, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (e.g., through the Internet using an Internet service provider). In some implementations, an electronic circuit, including, for example, a programmable logic circuit, a field-programmable gate array (FPGA), or a programmable logic array (PLA), can be personalized by utilizing the state information of the computer-readable program instructions to execute the computer-readable program instructions to perform the various aspects.

[0245] In this document, various aspects are described with reference to flowchart illustrations or block diagrams of methods, apparatuses (systems), and computer program products according to various embodiments. It should be understood that each block in the flowchart illustration or block diagram, and combinations of blocks in the flowchart illustration or block diagram, can be implemented by computer-readable program instructions. These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions executed via the processor of the computer or other programmable data processing apparatus create means for implementing the functions / actions specified in one or more blocks of the flowchart or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium that can direct a computer, a programmable data processing apparatus, or other devices to operate in a particular manner, the computer-readable storage medium including an article of manufacture that includes instructions for implementing aspects of the functions / actions specified in one or more blocks of the flowchart or block diagram. The computer-readable program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus, or other devices, thereby producing a computer-implemented process such that the instructions executed on the computer, other programmable apparatus, or other devices implement the functions / actions specified in one or more blocks of the flowchart or block diagram.

[0246] The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments. In this regard, each block in the flowchart or block diagram may represent a module, segment, or portion of instructions, which includes one or more executable instructions for implementing the specified logical function. In some alternative implementations, the functions noted in the blocks may occur out of the order noted in the figures. For example, two consecutive blocks shown may actually be executed substantially concurrently, or sometimes in reverse order, depending on the functions involved. It should also be noted that each block of the block diagrams or flowchart illustrations, and combinations of blocks in the block diagrams or flowchart illustrations, can be implemented by a special-purpose hardware system that performs the specified functions or actions, or a combination of special-purpose hardware and computer instructions.

[0247] Although the subject matter has been described above in the general context of computer-executable instructions of a computer program product that runs on a computer, those skilled in the art will recognize that the present disclosure may also be implemented in whole or in part in conjunction with other program modules. Generally, program modules include routines, programs, components, data structures, etc. that perform particular tasks or implement particular abstract data types. Additionally, those skilled in the art will recognize that various aspects may be practiced using other computer system configurations, including single-processor or multi-processor computer systems, minicomputers, mainframe computers, as well as computers, hand-held computing devices (e.g., PDAs, cell phones), microprocessor-based or programmable consumer or industrial electronic products, and the like. The illustrated aspects may also be practiced in a distributed computing environment where tasks are performed by remote processing devices that are linked through a communications network. However, some (if not all) aspects of the present disclosure may be practiced on stand-alone computers. In a distributed computing environment, program modules may be located in both local and remote memory storage devices.

[0248] As used in this application, the terms "component", "system", "platform", "interface", etc. may refer to or may include a computer-related entity or an entity related to an operating machine with one or more specific functions. Entities disclosed herein may be hardware, a combination of hardware and software, software, or software in execution. For example, a component may be, but is not limited to, a process running on a processor, a processor, an object, an executable program, a thread of execution, a program, or a computer. By way of illustration, both an application running on a server and the server can be a component. One or more components may reside within a process or thread of execution, and a component may be located on one computer or distributed between two or more computers. As another example, corresponding components may be executed from various computer-readable media that store various data structures. These components may communicate, such as via a signal having one or more data packets, through local or remote processes (e.g., data from one component interacts with another component in a local system, a distributed system, or across a network such as the Internet with other systems). As another example, a component may be a device having specific functionality provided by a mechanical component operated by an electrical or electronic circuit, where the electrical or electronic circuit is operated by a software or firmware application executed by a processor. In such a case, the processor may be internal or external to the device and may execute at least a portion of the software or firmware application. As yet another example, a component may be a device that provides specific functionality through electronic components without a mechanical component, where the electronic components may include a processor or other means for executing software or firmware that at least partially imparts functionality to the electronic components. In one aspect, a component may be simulated via, for example, a virtual machine within a cloud computing system.

[0249] In addition, the term "or" is intended to mean inclusive "or" rather than exclusive "or". That is, unless otherwise specified or clear from the context, "X uses A or B" is intended to mean any natural inclusive permutation. That is, if X adopts A; X adopts B; or X adopts both A and B, then "X uses A or B" is satisfied in any of the foregoing examples. As used herein, the term "and / or" is intended to have the same meaning as "or". In addition, unless otherwise specified or clear from the context with respect to the singular form, the article "a" as used in this specification and the drawings should generally be construed to mean "one or more". As used herein, the terms "example" or "exemplary" are used to denote an example, instance, or illustration. For the avoidance of doubt, the subject matter disclosed herein is not limited by such examples. In addition, any aspect or design described herein as "example" or "exemplary" is not necessarily to be construed as more preferred or advantageous than other aspects or designs, nor does it imply the exclusion of equivalent exemplary structures and techniques known to those of ordinary skill in the art.

[0250] The disclosure herein describes non - limiting examples. For ease of description or explanation, the term "each", "every", or "all" is used in discussing various examples in the parts disclosed herein. The use of terms such as "each", "every", or "all" is not restrictive. In other words, when the disclosure herein provides a description of "each", "all", or "every" applicable to a particular object or component, it should be understood that this is only a non - limiting example, and it should also be understood that in various other examples, such a description may apply to less than "each", "all", or "every" of that particular object or component.

[0251] As used in this specification, the term "processor" may refer to substantially any computing processing unit or device, including but not limited to a single-core processor; a single processor with software multithreaded execution capabilities; a multi-core processor; a multi-core processor with software multithreaded execution capabilities; a multi-core processor with hardware multithreaded technology; a parallel platform; and a parallel platform with distributed shared memory. Additionally, a processor may refer to an integrated circuit, an application specific integrated circuit (ASIC), a digital signal processor (DSP), a field programmable gate array (FPGA), a programmable logic controller (PLC), a complex programmable logic device (CPLD), discrete gate or transistor logic components, discrete hardware components, or any combination thereof that is designed to perform the functions described herein. Further, a processor may utilize nanoscale architectures, such as (but not limited to) molecular and quantum dot-based transistors, switches, and gates, to optimize space usage or enhance the performance of user equipment. A processor may also be implemented as a combination of computing processing units. In the present disclosure, terms such as "repository", "storage device", "data repository", "data storage device", "database", and substantially any other information storage component related to the operation and function of a component are used to refer to "memory components", entities embodied in "memory", or components that include memory. It should be understood that the memory and / or memory components may be volatile memory or non-volatile memory, or may include both volatile memory and non-volatile memory. By way of illustration and not limitation, non-volatile memory may include read only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable ROM (EEPROM), flash memory, or non-volatile random access memory (RAM) (e.g., ferroelectric RAM (FeRAM)). Volatile memory may include RAM, which may, for example, act as an external cache memory. By way of example and not limitation, RAM takes many forms, such as synchronous RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), direct Rambus RAM (DRRAM), direct Rambus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM). Additionally, the memory components of the systems disclosed herein or computer-implemented methods are intended to include, but not be limited to, these and any other suitable types of memory.

[0252] The foregoing content only includes examples of systems and computer-implemented methods. Of course, it is not possible to describe every conceivable combination of components or computer-implemented methods for the purpose of describing the present disclosure, but many further combinations and permutations of the present disclosure are possible. In addition, with respect to the use of the terms "comprising", "having", "owning", etc. in the detailed description, claims, appendices, and drawings, these terms are intended to be inclusive in a manner similar to the way the term "comprising" is interpreted when used as a transitional word in a claim.

[0253] The descriptions of the various embodiments have been presented for purposes of illustration, but are not intended to be exhaustive or limited to the embodiments disclosed herein. Many modifications and variations will be apparent without departing from the scope and spirit of the described embodiments. The terms used herein were chosen to best explain the principles of the embodiments, the practical application, or a technical improvement over the technology found in the marketplace, or to enable other ordinary skilled artisans in the art to understand the embodiments disclosed herein.

[0254] Various non-limiting aspects are described in the following examples.

[0255] Example 1: A scientific instrument can include a processor that can execute computer-executable components stored in a non-transitory computer-readable memory. In various aspects, the computer-executable components can include an access component that can access a digital twin of the scientific instrument. In various cases, the computer-executable components can include a calibration component that can synchronize the parameter state of the digital twin with the physical state of the scientific instrument via the execution of a full-state Bayesian filter.

[0256] Example 2: A scientific instrument according to any of the preceding examples can be implemented, wherein the full-state Bayesian filter can include a set of calibration iterations, and each calibration iteration in the set of calibration iterations can include a Bayesian update of all the parameter states based on iterative shared observables that can be demonstrated by the scientific instrument and simulated by the digital twin.

[0257] Example 3: A scientific instrument capable of implementing any of the foregoing embodiments, wherein during a current calibration iteration in the set of calibration iterations, the calibration component is capable of: measuring an observation result of the iteratively shared observable exhibited during operation of the scientific instrument; randomly sampling a plurality of parameter state instantiations from the state space of the digital twin, wherein the state space can be progressively constrained via recursive Bayesian update based on a previous calibration iteration; calculating a plurality of simulated observation results of the iteratively shared observable based on running the plurality of parameter state instantiations on the digital twin; assigning weights to the plurality of parameter state instantiations respectively based on the difference between the observation result and the plurality of simulated observation results; deleting any parameter state instantiations having a weight below a threshold weight value among the plurality of parameter state instantiations, thereby generating a plurality of remaining parameter state instantiations; checking whether the variance of the plurality of remaining parameter state instantiations is below a threshold variance value; in response to determining that the variance of the plurality of remaining parameter state instantiations is below the threshold variance value, determining that the parameter state of the digital twin and the physical state of the scientific instrument are synchronized at the weighted average of the plurality of remaining parameter state instantiations; in response to determining that the variance of the plurality of remaining parameter state instantiations is not below the threshold variance value, applying an active setting adjustment to the scientific instrument; and modifying the plurality of remaining parameter state instantiations based on the active setting adjustment, thereby generating a plurality of modified remaining parameter state instantiations, wherein the plurality of modified remaining parameter state instantiations are used to progressively constrain the state space via recursive Bayesian update during a subsequent calibration iteration.

[0258] Example 4: A scientific instrument capable of implementing any of the foregoing embodiments, wherein the calibration component is further capable of modifying the plurality of remaining parameter state instantiations based on passive time evolution associated with the scientific instrument.

[0259] Example 5: A scientific instrument capable of implementing any of the foregoing embodiments, wherein the scientific instrument can be a charged particle microscope.

[0260] Example 6: A scientific instrument capable of implementing any of the foregoing embodiments, wherein the parameter state of the digital twin can be an aberration coefficient vector of the charged particle microscope.

[0261] Example 7: A scientific instrument capable of implementing any of the foregoing embodiments, wherein the iteratively shared observable can be based on a convergent beam electron diffraction pattern of an amorphous carbon sample captured by the charged particle microscope.

[0262] Example 8: A scientific instrument capable of implementing any of the foregoing embodiments, wherein the active setting adjustment can be a lens setting adjustment, a deflector setting adjustment, a temperature setting adjustment, or a stage actuator adjustment of the charged particle microscope.

[0263] Example 9: A scientific instrument capable of implementing any of the foregoing embodiments, wherein the full-state Bayesian filter can be based on particle filter technology or Kalman filter technology.

[0264] In various embodiments, any one or more combinations of Examples 1 to 9 can be implemented.

[0265] Example 10: A computer-implemented method can include synchronizing the parameter state of a digital twin with the physical state of a scientific instrument by a device operatively coupled to a processor that executes a full-state Bayesian filter. In various aspects, the computer-implemented method can include generating, by the device and in response to the synchronization, an electronic alert indicating that the digital twin is ready to predict the behavior of the scientific instrument.

[0266] Example 11: A computer-implemented method capable of implementing any of the foregoing embodiments, wherein the full-state Bayesian filter can include a set of calibration iterations, and each calibration iteration in the set of calibration iterations can include a Bayesian update of all the parameter states based on iterative shared observables that can be exhibited by the scientific instrument and simulated by the digital twin.

[0267] Example 12: Capable of implementing the computer-implemented method according to any of the foregoing embodiments, wherein the current calibration iteration in the set of calibration iterations can include: measuring, by the device, an observation result of the iteratively shared observable exhibited during the operation of the scientific instrument; randomly sampling, by the device, a plurality of parameter state instantiations from the state space of the digital twin, wherein the state space can be progressively constrained via recursive Bayesian update based on a previous calibration iteration; calculating, by the device and based on running the plurality of parameter state instantiations on the digital twin, a plurality of simulated observation results of the iteratively shared observable; assigning, by the device, weights to the plurality of parameter state instantiations respectively based on a difference between the observation result and the plurality of simulated observation results; deleting, by the device, any parameter state instantiation having a weight below a threshold weight value among the plurality of parameter state instantiations, thereby generating a plurality of remaining parameter state instantiations; checking, by the device, whether a variance of the plurality of remaining parameter state instantiations is below a threshold variance value; determining, by the device and in response to determining that the variance of the plurality of remaining parameter state instantiations is below the threshold variance value, that the parameter state of the digital twin and the physical state of the scientific instrument are synchronized at a weighted average of the plurality of remaining parameter state instantiations; determining, by the device and in response to determining that the variance of the plurality of remaining parameter state instantiations is not below the threshold variance value, applying an active setting adjustment to the scientific instrument; and modifying, by the device and based on the active setting adjustment, the plurality of remaining parameter state instantiations, thereby generating a plurality of modified remaining parameter state instantiations, wherein the plurality of modified remaining parameter state instantiations can be used to progressively constrain the state space via recursive Bayesian update during a subsequent calibration iteration.

[0268] Example 13: Capable of implementing the computer-implemented method according to any of the foregoing embodiments, wherein the device can further modify the plurality of remaining parameter state instantiations based on a passive time evolution associated with the scientific instrument.

[0269] Example 14: Capable of implementing the computer-implemented method according to any of the foregoing embodiments, wherein the scientific instrument can be a charged particle microscope.

[0270] Example 15: Capable of implementing the computer-implemented method according to any of the foregoing embodiments, wherein the parameter state of the digital twin can be an aberration coefficient vector of the charged particle microscope.

[0271] Example 16: Capable of implementing the computer-implemented method according to any of the foregoing embodiments, wherein the iteratively shared observable can be based on a convergent beam electron diffraction pattern of an amorphous carbon sample captured by the charged particle microscope.

[0272] Example 17: A computer-implemented method according to any of the foregoing examples can be implemented, wherein the active setting adjustment can be a lens setting adjustment, a deflector setting adjustment, a temperature setting adjustment, or a stage actuator adjustment of the charged particle microscope.

[0273] In various embodiments, any one or more combinations of Examples 10 to 17 can be implemented.

[0274] Example 18: A computer program product for facilitating improved digital twin calibration for a scientific instrument can include a non-transitory computer-readable memory having program instructions stored therein. In various aspects, the program instructions can be executable by a processor to cause the processor to perform the following operations: access a digital twin of a charged particle microscope; synchronize the parameter state of the digital twin with the physical state of the charged particle microscope via the execution of a set of calibration iterations, each calibration iteration of the set of calibration iterations can include a Bayesian update of all the parameter states based on an iteratively shared observable that can be exhibited by the charged particle microscope and simulated by the digital twin; and forecast how the charged particle microscope will respond to a proposed usage scenario by running the digital twin after synchronization.

[0275] Example 19: A computer program product according to any of the foregoing examples can be implemented, wherein the parameter state of the charged particle microscope can be an aberration coefficient vector.

[0276] Example 20: A computer program product according to any of the foregoing examples can be implemented, wherein the iteratively shared observable can be based on a convergent beam electron diffraction pattern of an amorphous carbon sample. In various embodiments, any one or more combinations of Examples 18 to 20 can be implemented. In various embodiments, any one or more combinations of Examples 1 to 20 can be implemented.

Claims

1. A scientific instrument, comprising: a processor that executes computer-executable components stored in a non-transitory computer-readable memory, wherein the computer-executable components include: an access component that accesses a digital twin of the scientific instrument; and a calibration component that synchronizes parameter states of the digital twin with physical states of the scientific instrument via execution of a full-state Bayesian filter.

2. The scientific instrument according to claim 1, wherein the full-state Bayesian filter includes a set of calibration iterations, and each calibration iteration in the set of calibration iterations includes a Bayesian update of all the parameter states based on iteratively shared observables that can be demonstrated by the scientific instrument and simulated by the digital twin.

3. The scientific instrument according to claim 2, wherein during a current calibration iteration in the set of calibration iterations, the calibration component: measures an observation of the iteratively shared observables demonstrated during operation of the scientific instrument; randomly samples a plurality of parameter state instantiations from a state space of the digital twin, wherein the state space is progressively constrained via recursive Bayesian updates based on a previous calibration iteration; calculates a plurality of simulated observations of the iteratively shared observables based on running the plurality of parameter state instantiations on the digital twin; assigns weights to the plurality of parameter state instantiations respectively based on a difference between the observations and the plurality of simulated observations; deletes any parameter state instantiations having weights below a threshold weight value among the plurality of parameter state instantiations, thereby generating a plurality of remaining parameter state instantiations; checks whether a variance of the plurality of remaining parameter state instantiations is below a threshold variance value; in response to determining that the variance of the plurality of remaining parameter state instantiations is below the threshold variance value, determines that the parameter states of the digital twin and the physical states of the scientific instrument are synchronized at a weighted average of the plurality of remaining parameter state instantiations; in response to determining that the variance of the plurality of remaining parameter state instantiations is not below the threshold variance value, applies an active setting adjustment to the scientific instrument; and modifies the plurality of remaining parameter state instantiations based on the active setting adjustment, thereby generating a plurality of modified remaining parameter state instantiations, wherein the plurality of modified remaining parameter state instantiations are used to progressively constrain the state space via recursive Bayesian updates during a subsequent calibration iteration.

4. The scientific instrument according to claim 3, wherein the calibration component further modifies the plurality of remaining parameter state instantiations based on a passive time evolution associated with the scientific instrument.

5. The scientific instrument according to claim 3, wherein the scientific instrument is a charged particle microscope.

6. The scientific instrument according to claim 5, wherein the parameter state of the digital twin is an aberration coefficient vector of the charged particle microscope.

7. The scientific instrument according to claim 5, wherein the iterative shared observable is based on a convergent beam electron diffraction pattern of an amorphous carbon sample captured by the charged particle microscope.

8. The scientific instrument according to claim 5, wherein the active setting adjustment is a lens setting adjustment, a deflector setting adjustment, a temperature setting adjustment, or a stage actuator adjustment of the charged particle microscope.

9. The scientific instrument according to claim 1, wherein the full state Bayesian filter is based on particle filter technology or Kalman filter technology.

10. A computer-implemented method, comprising: synchronizing, by a device operatively coupled to a processor that executes a full state Bayesian filter, a parameter state of a digital twin with a physical state of a scientific instrument; and generating, by the device and in response to the synchronization, an electronic alert indicating that the digital twin is ready to predict the behavior of the scientific instrument.

11. The computer-implemented method according to claim 10, wherein the full state Bayesian filter includes a set of calibration iterations, and each calibration iteration in the set of calibration iterations includes a Bayesian update of all the parameter states based on an iterative shared observable that can be demonstrated by the scientific instrument and simulated by the digital twin.

12. The computer-implemented method according to claim 11, wherein the current calibration iteration in the set of calibration iterations includes: measuring, by the device, an observation result of the iterative shared observable demonstrated during operation of the scientific instrument; randomly sampling, by the device, a plurality of parameter state instantiations from a state space of the digital twin, wherein the state space is progressively constrained via recursive Bayesian update based on a previous calibration iteration; computing, by the device and based on running the plurality of parameter state instantiations on the digital twin, a plurality of simulated observation results of the iterative shared observable; assigning, by the device, weights to the plurality of parameter state instantiations respectively based on a difference between the observation result and the plurality of simulated observation results; deleting, by the device, any parameter state instantiation having a weight below a threshold weight value among the plurality of parameter state instantiations, thereby generating a plurality of remaining parameter state instantiations; checking, by the device, whether a variance of the plurality of remaining parameter state instantiations is below a threshold variance value; determining, by the device and in response to determining that the variance of the plurality of remaining parameter state instantiations is below the threshold variance value, that the parameter state of the digital twin and the physical state of the scientific instrument are synchronized at a weighted average of the plurality of remaining parameter state instantiations; applying, by the device and in response to determining that the variance of the plurality of remaining parameter state instantiations is not below the threshold variance value, an active setting adjustment to the scientific instrument; and The device modifies the instantiation of the plurality of remaining parameter states based on the active setting adjustment, thereby generating a plurality of modified remaining parameter state instantiations, wherein the plurality of modified remaining parameter state instantiations are used to progressively constrain the state space via recursive Bayesian updates during a subsequent calibration iteration.

13. The computer-implemented method according to claim 12, wherein the device further modifies the instantiation of the plurality of remaining parameter states based on the passive time evolution associated with the scientific instrument.

14. The computer-implemented method according to claim 12, wherein the scientific instrument is a charged particle microscope.

15. The computer-implemented method according to claim 14, wherein the parameter state of the digital twin is the aberration coefficient vector of the charged particle microscope.

16. The computer-implemented method according to claim 14, wherein the iterative shared observable is based on the convergent beam electron diffraction pattern of an amorphous carbon sample captured by the charged particle microscope.

17. The computer-implemented method according to claim 14, wherein the active setting adjustment is a lens setting adjustment, a deflector setting adjustment, a temperature setting adjustment, or a stage actuator adjustment of the charged particle microscope.

18. A computer program product for facilitating improved calibration of a digital twin for a scientific instrument, the computer program product comprising a non-transitory computer-readable memory containing program instructions that can be executed by a processor to cause the processor to: access a digital twin of a charged particle microscope; synchronize the parameter state of the digital twin with the physical state of the charged particle microscope via the execution of a set of calibration iterations, each calibration iteration in the set of calibration iterations including a Bayesian update of all the parameter states based on an iterative shared observable that can be demonstrated by the charged particle microscope and simulated by the digital twin; and forecast how the charged particle microscope will respond to a proposed usage scenario by running the digital twin after synchronization.

19. The computer program product according to claim 18, wherein the parameter state of the charged particle microscope is the aberration coefficient vector.

20. The computer program product according to claim 19, wherein the iterative shared observable is based on the convergent beam electron diffraction pattern of an amorphous carbon sample.

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