270 results about "Surrogate model" patented technology

A method for optimizing a solution set comprises the steps of generating an initial solution set, identifying a desirable portion of the initial solution set using a fitness calculator, using the desirable portion to create a surrogate fitness model that is computationally less expensive than the fitness calculator, generating new solutions, replacing at least a portion of the initial solution set with the new solutions to create a second solution set, and evaluating at least a portion of the second solution set with the fitness surrogate model to identify a second desirable portion.

Methods for creating and using space-time surrogate models of subsurface regions, such as subsurface regions containing at least one hydrocarbon formation. The created surrogate models are explicit models that may be created from implicit models, such as computationally intensive full-physics models. The space-time surrogate models are parametric with respect to preselected variables, such as space, state, and/or design variables, while also indicating responsiveness of the preselected variables with respect to time. In some embodiments, the space-time surrogate model may be parametric with respect to preselected variables as well as to time. Methods for updating and evolving models of subsurface regions are also disclosed.

A method and system for personalized non-invasive assessment of renal artery stenosis for a patient is disclosed. Medical image data of a patient is received. Patient-specific renal arterial geometry of the patient is extracted from the medical image data. Features are extracted from the patient-specific renal arterial geometry of the patient. A hemodynamic index is computed for one or more locations of interest in the patient-specific renal arterial geometry based on the extracted features using a trained machine-learning based surrogate model. The machine-learning based surrogate model is trained based on features extracted from synthetically generated renal arterial geometries.

A method of designing or selecting an implantable medical device comprises the steps of: i) obtaining a plurality of measured data points of a characteristic of an anatomical feature of an individual; ii) using said data points to construct a surrogate model of said characteristic, the surrogate model being constructed by interpolating or regressing measured data points of the characteristic, and using said surrogate model to obtain predicted values of said characteristic at a plurality of locations; iii) using said predicted values to determine or select at least one value of a design parameter of the implantable medical device. There is further disclosed a method of monitoring or diagnosing a disease or disorder.

A method of optimising a sequential combinatorial process comprising an interchangeable sequence of events comprises using a master model to model a selection of the possible sequences, and using information derived from the master model in a surrogate model that approximates the master model with a much shorter computation time. The surrogate model calculates all the possible sequences using an algorithm to select from the information calculated by the master model that which most closely matches the events of a present sequence, following a prioritised system so that the best match is used wherever possible. All results from the surrogate model are compared so that the modelled sequence that gives the result closest to a desired optimum result for the process can be identified, and potentially applied to the process. Accuracy can be enhanced y running the optimum sequence through the master model as a check, and further by adding the optimum sequence to the information used by the surrogate model in future calculations. Any sequential combinatorial process, in which the quality of the end result of the process depends on the order in which events in the process are performed, can be optimised in this way, including manufacturing process such as machining, cutting, shaping, forming and/or heat treating a workpiece, flow of material through a factory or oil or gas through a pipeline network, chemical and material science mixing processes, computational biology modelling, and fleet management.

Systems and methods which implement surrogate (e.g., approximation) models to systematically reduce the parameter space in an optimization problem are shown. In certain embodiments, rigorous (e.g., higher fidelity) models are implemented with respect to the reduced parameter space provided by use of surrogate models to efficiently and more rapidly arrive at an optimized solution. Accordingly, certain embodiments build surrogate models of an actual simulation, and systematically reduce the number of design parameters used in the actual simulation to solve optimization problems using the actual simulation. A multi-stage method that facilitates optimization of decisions related to development planning and reservoir management may be provided. Iterative processing may be implemented with respect to a multi-stage optimization method. There may be uncertainty in various parameters, such as in reservoir parameters, which is taken into account according to certain embodiments.

A method and system for estimating tissue parameters of a computational model of organ function and their uncertainty due to model assumptions, data noise and optimization limitations is disclosed. As applied to a cardiac use-case, a patient-specific anatomical heart model is generated from medical image data of a patient. A patient-specific computational heart model is generated based on the patient-specific anatomical heart model. Patient-specific parameters and corresponding uncertainty values are estimated for at least a subset of parameters of the patient-specific computational heart model. A surrogate model is estimated for a forward model of cardiac function, and the surrogate model is applied within Bayesian inference to estimate the posterior probability density function of the parameter space of the forward model. Cardiac function for the patient is simulated using the patient-specific computational heart model. The estimated parameters, their uncertainty, and the computed cardiac function are displayed to the user.

The invention relates to an optimum design method for an aircraft system based on a sequence radial basis function surrogate model and belongs to the technical field of optimum design of the aircraft system. According to the optimum design method, the thought of searching step range of trust domain solving high-dimensional optimization problem is applied to a supervisory sequence surrogate model, the sample space is gradually updated and the approximate precision of the surrogate model is promoted, so that the optimizing strategy convergence is guided to the globally optimal solution and the optimum design method provided by the invention has better global optimization capacity and optimization efficiency. The method is strong in universality. According to the method, the program development is conveniently realized, the optimum design means for the aircraft system is improved, the design cost is lowered and the requirement for multidisciplinary optimum design of the present aircraft system is met.

The invention discloses a test run method for an accelerated life test of a gas turbine engine. The test run method comprises the steps that an accelerated factor analysis model of the gas turbine engine under simulated running conditions is established; according to the accelerated factor analysis model, a support vector machine method is used for establishing a surrogate model of three-dimensional finite element stress analysis; the surrogate model is used for obtaining dangerous point stress of the accelerated factor analysis model, and then life indexes of the accelerated factor analysis model are obtained; a Monte Carlo method is used for conducting stochastic simulation on random variables of the life indexes; an optimal design mathematical model of an accelerated life test scheme of stress under the simulated running conditions of the gas turbine engine is established and analyzed, and the test scheme is optimized according to the optimal design mathematical model; a mixing optimization method is adopted to optimize the mathematical model, and the optimized accelerated life test scheme is obtained according to the optimized mathematical model. By means of the test run method, the test period is shortened, and life test expenditure is reduced.

A system includes a schedule generator having one or more processors configured to obtain resource extraction parameters for extracting a resource from a reservoir. The resource extraction parameters include well creation parameters associated with drilling wellbores, well stimulation parameters associated with introducing fracturing fluid into the wellbores, and production parameters associated with extracting the resource through the wellbores. The schedule generator selects initial trial schedules having different values of the resource extraction parameters and receives initial resource output data generated by execution of the initial trial schedules with a designated reservoir model. The schedule generator generates a surrogate model based on the initial resource output data and the initial trial schedules and uses the surrogate model to perform iterations of selecting modified trial schedules until a predetermined condition is satisfied.

The invention provides a helicopter moving part service life management method based on digital twinning. The helicopter moving part service life management method comprises the steps of load data collection, structural stress analysis, crack parametric modeling, fracture mechanics simulation, agent model construction of fracture mechanics parameter prediction, probabilistic prediction of crack propagation, structural risk evaluation and dynamic adjustment of a maintenance inspection plan. The invention relates to an electronic device and a storage medium, which are used for executing a helicopter moving part life management method based on digital twinning. According to the invention, multiple scales of the structure are considered and multiple models are fused; and establishing a digitaltwinning body of the typical moving part structure of the helicopter, performing high-performance real-time simulation according to the flight parameters of the helicopter and the load and damage data of the key part of the structure, dynamically updating the damage state of the typical moving part of the helicopter, and adjusting the residual life and the maintenance and inspection plan of the structure.

The invention discloses a real-time yield prediction method for a hydrocracking device. Field real-time data are processed with a data reconciliation technology, and hydrocracking reaction kinetics parameters are corrected in real time in combination with an improved differential evolution algorithm, so that a mechanism model can accurately describe the actual running condition of the device. On the basis of the corrected model, effects caused by key operation/process conditions such as the raw material density, the sulfur content, the nitrogen content, the reaction temperature, the pressure, the hydrogen-to-oil volume ratio and the like on hydrocracked products are analyzed. Piecewise linearization is performed according to the effect trend, a linear equation is solved, corresponding Delta-Base yield data are acquired, the operation condition is associated with the Delta-Base data with a neutral network modeling technology, a yield surrogate model is established, the yield data calculation speed is increased, real-time prediction of the yield of products of the hydrocracking device is realized, and theoretical support is provided for establishing an accurate plan optimization PIMS (process industry modeling system) model.

The invention relates to the technical field of bearing designing, in particular to a multi-objective optimization design method of a spiral oil wedge bearing. According to the method, an oil film property calculation method based on solving a Navier-Stokes function is used for calculating the oil film properties of the spiral oil wedge bearing, a minitype multi-objective genetic algorithm is used for conducting multi-objective optimization design of a bearing structure, and a radial basis function surrogate model is established in the optimization process for calculating. The method comprises the following specific steps of establishing a mathematical model of a spiral oil wedge bearing multi-objective optimization design problem, sampling experimental design methods, establishing the radial basis function surrogate model, evaluating the precision of the surrogate model, using the minitype multi-objective genetic algorithm for solving the multi-objective optimization design problem and acquiring an optimal compromise solution. The multi-objective optimization design method is suitable for the multi-objective project optimization problem of the spiral oil wedge bearing, high global optimization capacity is achieved, the optimization efficiency can be effectively improved, the solving efficiency and the calculation precision of the algorithm are improved, and the time for calculation is little.

A method and system for determining hemodynamic indices, such as fractional flow reserve (FFR), for a location of interest in a coronary artery of a patient are disclosed. Medical image data of a patient is received. Patient-specific coronary arterial tree geometry of the patient is extracted from the medical image data. Geometric features are extracted from the patient-specific coronary arterial tree geometry of the patient. A hemodynamic index, such as FFR, is computed for a location of interest in the patient-specific coronary arterial tree based on the extracted geometric features using a trained machine-learning based surrogate model. The machine-learning based surrogate model is trained based on geometric features extracted from synthetically generated coronary arterial tree geometries.

The invention relates to a complete period dynamic optimization method for an industrial ethylene cracking furnace and based on a surrogate model. By simulating the mechanism of the industrial ethylene cracking furnace and utilizing an experimental design principle, a certain amount of simulation data is generated, then a neural network surrogate model is utilized to carry out modeling. By employing the neural network surrogate model and combining iterative computation update of the thickness of a coking layer in a furnace tube, a complete period dynamic model of the cracking furnace is obtained. Based on the complete period dynamic model of the cracking furnace, the complete period dynamic optimization method for the cracking furnace is provided, then a constructed continuous dynamic optimization model is approximately solved by means of a piecewise linear representation method. Compared with experiential operation, dynamic optimization is substantially greater in economic benefit. The complete period dynamic optimization method is simple in theory, reasonable in derivation, high in engineering usability, easy to transplant and wide in adaptability, and is simple and practicable.

The invention discloses a parameter optimization method of Modelica model based on surrogate model, which comprises the following steps: 1, compiling Modelica model and obtaining model parameter and variable information; 2, optimizing modeling; 3, generating sampling point; 4, carry out simulation calculation on that parameter combination; 5, analyzing that simulation calculation result; 6, constructing a proxy model; 7, using that surrogate model to replace the Modelica model to carry on the optimization iteration and find the optimal parameter; 8, carrying out simulation calculation on thatoptimal parameters, and if the error between the simulation calculation result and the output result of the proxy model is small than a set value, executing the step 10, otherwise executing the step 9; 9, dynamically updating the agent model according to the simulation calculation result of the step 8, and then executing the step 7; 10, the optimal parameter calculated in the step 7 is the final optimization result, and the parameter optimization is finished; Through the above steps, the invention achieves the purpose of improving the parameter optimization efficiency of the Modelica model, and solves the practical problem that the calculation amount in the parameter optimization process of the Modelica model is huge and it is difficult to optimize the parameters of the large-scale model.

The invention discloses a full-process modeling method for an oil refining process. Based on the mechanisms and running characteristics of all production devices in the oil refining process and a corrected model, the influence of key operation/process conditions of all the devices on the product yield is analyzed. Piecewise linearization is carried out according to the influence trend, a linear equation is solved, corresponding Delta-Base yield data are obtained, a neural network modeling technology is combined, operation conditions and the Delta-Base data are related, a yield surrogate model is built, the yield data calculating speed is improved, real-time prediction on the product yield in the oil refining process is achieved, and theoretical supports are provided for building a precise plan optimized PIMS model.

A method for determining a reduction factor of a bearing capacity of an axial load cylindrical shell structure relates to stability checking of main bearing strength thin-walled members of aerospace and architectural structures. Different from experiment experience-based conventional defect sensitivity evaluating method represented by NASA SP-8007, a depression defect is introduced in a manner of applying a radial disturbance load. First, an influence rule of a depression defect amplitude of a single point to an axial load bearing capacity is analyzed by using numerical values, so as to determine a load amplitude range; then, defect sensitivity analysis is performed on depression defects of multiple points; then, experiment design sampling is performed by using load amplitude values and load position distribution as design variables; and finally, based on optimizing technologies such as an enumeration method, a genetic algorithm and a surrogate model, the most disadvantageous disturbance load of the multiple points that limits the defect amplitude is searched for, and a reduction factor of the bearing capacity of the axial load cylindrical shell structure is determined, so as to establish a more physical method for evaluating the defect sensitivity and the bearing performance of the axial load cylindrical shell structure.

A computer-implemented method for personalized assessment of a subject's bone health includes extracting a plurality of features of interest from non-invasive subject data, medical images of the subject, and subject-specific bone turnover marker values. A surrogate model and the plurality of features of interest are used to predict one or more subject-specific measures of interest related to bonehealth. Then, a visualization of the one or more subject-specific measures of interest related to bone health is generated.

The invention provides an electromechanical cooperative design method of a skin antenna based on parallel Bayesian optimization in order to reduce calculation amount, improve design quality and optimize efficiency. Determine the design variables and initial design space, carry out initial sampling, extract all sample points in the sample point database and their corresponding response values, select Bayesian algorithm to construct the surrogate model of the objective function, use the pseudo-expectation enhancement criterion as the collection function to select multiple candidate solutions, and judge the degree of satisfaction of the constraint conditions. If the number of calculation times of the electromechanical coupling model reaches the set number or satisfies the convergence criterion, the iteration is stopped and the optimal solution is outputted. A large amount of optimization time is saved for complex problems, and only one sample point is randomly selected for initial sampling, so the optimization process is repeatable.

The invention discloses a method for optimizing a reconstruction model of disturbing gravity along a gliding trajectory. The method comprises the following steps of firstly, computing a three-dimensional envelop of a trajectory of a pole changing coordinate system; secondly, dividing the whole airspace of the pole changing coordinate system; thirdly, establishing a whole reconstruction model of the disturbing gravity of a general coordinate system; fourthly, establishing a local reconstruction model of the disturbing gravity along the trajectory; fifthly, establishing a fast approximation algorithm of the disturbing gravity in a flight process based on this; sixthly, carrying out experiment design and constructing an agent model of the reconstruction model; and lastly, establishing an optimization method for the agent model. According to the method for optimizing the reconstruction model of the disturbing gravity along the gliding trajectory, a minimal storage capacity model under the given accuracy requirement and a highest accuracy model under the given memory space requirement can be obtained, the requirements of onboard real-time computation for data storage capacity, the computation speed and the reconstruction accuracy can be met, and the method has better prospect in engineering application.