29 results about "Physical computation" patented technology

The invention relates to the technical field of gamma radiation dose calculation, and particularly discloses a gamma radiation dose calculation method based on a Unity engine, a program, equipment and a storage medium. In order to solve the problems that ray tracing is complex and low in efficiency in complex irregular geometry by means of an existing point kernel integration method, the physical calculation capacity of a Unity engine is introduced, ray tracing is conducted through Physics. Raycast interface iteration, and gamma radiation dose calculation under the multi-layer shielding condition is optimized. The method comprises the following steps: acquiring radioactive source energy, intensity, shielding body density, mass attenuation coefficient and detection point coordinates; constructing a radioactive source model and generating a point kernel set through parallel array ray discretization; performing ray tracing on the point core set to obtain a list of all intersection points of rays and the shielding body; and calculating the shielding thickness of each layer according to the intersection point list, calculating the dose contribution of each point core in combination with the accumulation factor, and summing to obtain a total dose value.

The invention discloses a virtual character behavior processing method and system based on semantic decoupling and elastic coupling and a computer readable storage medium. According to the method, unstructured action data is decomposed into physical layer intention descriptors, measurable style descriptors and high-dimensional semantic layer features by utilizing a parallel physical calculation engine and an AI semantic analysis engine through a double-track feature decoupling mechanism. Besides, an elastic coupling mechanism between intentions and styles is introduced, style parameters are dynamically clamped based on a physical priority principle, and physical topology collapse caused by style overload is prevented. According to the method, a physical-semantic dual index system is constructed, database autonomous evolution based on manifold density analysis is supported, and the problems that in the prior art, virtual character action generation is poor in physical controllability, semantic understanding is lacked, and cross-scene generalization ability is weak are effectively solved.

The present application relates to the technical field of short-circuit current calculation, and particularly relates to a flexible DC system short-circuit current calculation method based on a physical information neural network, comprising: setting a model input feature vector, the model input feature vector being used to represent a system operating state before a fault and fault information, and performing data preprocessing on the model input feature vector; constructing a hybrid driving calculation model, the hybrid driving calculation model comprising a physical calculation module and a neural network module, and being coupled based on a preset fusion architecture; performing end-to-end training and optimization on the hybrid driving calculation model by using a preset composite loss function; and performing flexible DC system short-circuit current calculation based on the optimized hybrid driving calculation model, so that the problems of poor precision, speed and convergence, poor interpretability and weak generalization ability in the prior art are solved.

This invention provides an underwater long-range ultra-high-definition polarization imaging system and method, relating to the field of data processing technology. The system comprises, from bottom to top, an optical sensing layer, a physical computing layer, an AI inference enhancement layer, an adaptive control layer, and a decision optimization layer, forming a hierarchical control structure where data flows upward and strategies are passed downward. The optical sensing layer employs a non-uniform polarization sampling array, a spectral polarization joint coding unit, and a dual-modal illumination unit to simultaneously acquire intensity, multi-directional polarization, multispectral images, and depth information. The physical computing layer calculates polarization parameters in real time and inverts water turbidity within the image signal processor hardware pipeline. The AI ​​inference enhancement layer embeds the physical model as a differentiable module into a neural network to achieve scattering suppression and detail enhancement. The adaptive control layer adopts a time-scale separated dual-closed-loop architecture. This invention can adapt to different water turbidity levels, output ultra-high-definition images in real time, and significantly improve the quality of long-range underwater imaging.

The invention discloses an ultra-parallel microwave photon Isin machine system based on wavelength division multiplexing and an optimization method, and belongs to the technical field of photon calculation and combination optimization. The Isin machine system comprises a multi-wavelength light source module, a microwave modulation and wavelength division multiplexing coding module, a nonlinear calculation and evolution module and a demultiplexing and dynamic feedback module, all the modules are optically and electrically connected to form a closed-loop system, and different optimization problems can be coded to independent wavelength channels to achieve parallel solving. The optimization method is applied to the Isin machine system, and closed-loop optimization is completed through the steps of coupling matrix mapping, microwave modulation loading, nonlinear evolution, dynamic feedback control and the like. The method supports synchronous processing of 1024 optimization problems, and energy consumption of a single problem is lt; the method is mainly applied to the fields of logistics scheduling, financial modeling, communication network optimization and the like, and an efficient physical calculation solution is provided for a large-scale combination optimization problem.

This application provides a method, system, device, and medium for lead compound design based on deep reinforcement learning. It innovatively achieves de novo design of covalent inhibitors targeting affinity, proposes an innovative method for constructing novel drug molecules based on multiple different bioactive fragments, and optimizes the model's exploration space. The application of the physics computation software Autodock-GPU as a scoring function enhances the model's generalization ability and running speed. The application of Munchausen reinforcement learning techniques to the Soft Actor Critic discrete action space model improves the model's optimization performance. Compared to other models, this application expands and improves upon the model's functionality and the properties of the generated molecules.

A server includes physical compute nodes. Each physical compute node includes a host and physical management resources. The physical management resources include a physical management processor. The server includes a distributed hypervisor to provide a distributed application operating environment that is hosted by the physical management resources. The distributed hypervisor to allocate, from the physical management processors, virtual processors for the distributed application operating environment to execute applications to manage the physical compute nodes. The distributed hypervisor includes a plurality of hyper-kernels that are associated with respective physical compute nodes. Each hyper-kernel is hosted on the physical management resources of the associated physical compute node.

This invention discloses an industrial agent reasoning framework and method based on physical first-principle constraints, belonging to the field of industrial intelligence technology. The framework includes a data access layer, a task parsing layer, a candidate solution generation layer, a physical constraint verification layer, a conflict rollback and replanning layer, an execution decision layer, and a knowledge accumulation layer. The physical constraint verification layer uses at least one physical first-principle model as a pre-constraint hard constraint gating before the candidate decision sequence enters the execution decision layer. It performs parameter mapping, boundary condition solving, consistency calculation, residual determination, and executability determination on the physical computation variables obtained from the structured reasoning task mapping, and outputs structured conflict information when the verification fails. The conflict rollback and replanning layer performs targeted rollback and replanning based on the violation type, violation node level, and source of missing boundary conditions in the structured conflict information. The execution decision layer only outputs execution-type output results for candidate decision sequences that have obtained the pass flag. The knowledge accumulation layer writes the structured conflict information and the corrected pass results into a rule base, template base, or historical case base to serve as filtering conditions, initial parameter values, tool sorting criteria, or threshold setting criteria during subsequent candidate generation. This invention can improve the physical feasibility, reliability, interpretability, and auditability of industrial agent decision-making results.

The invention relates to the technical field of rock material elastic modulus field and Poisson's ratio field prediction, in particular to a rock elastic modulus and Poisson's ratio field prediction method based on physical constraints, which comprises the following steps: constructing a rock mechanical sample data set; the data set comprises a rock elastic modulus field, a Poisson's ratio field, a strain field, a stress field and space coordinates, a physical information neural network is constructed, an encoder, a decoder and a physical calculation layer are constructed, and the physical calculation layer is used for calculating a mechanical response strain field; the method comprises the steps of constructing a multi-element composite loss function fused with physical constraints on the basis of a physical information neural network, performing iterative training on the physical information neural network on the basis of the multi-element composite loss function, and outputting a high-resolution elastic modulus field and a Poisson's ratio field of a target rock test piece. It is ensured that the predicted elastic modulus and Poisson's ratio parameter field meets the basic law of rock mechanics, and the credibility of the prediction result is improved.

The invention relates to the field of reactor core physical calculation, in particular to a reactor core physical optimization calculation method under a nuclear power unit bias operation condition, and the method comprises the following steps: 1, constructing a geometric model and a reactor core grid model of a reactor in fluid dynamics software; 2, establishing a porous medium model in fluid dynamics software; 3, setting boundary conditions in the hydrodynamic software and carrying out thermal hydraulic calculation; 4, constructing a thermotechnical and physical coupling interface, taking a thermotechnical calculation result of the hydrodynamic software as input, and carrying out physical calculation in physical calculation software; 5, taking the three-dimensional power distribution of the physical calculation software as fluid dynamics software input, and carrying out thermal hydraulic calculation to obtain an optimized fuel assembly upper power peak factor and an optimized fuel assembly volume power peak factor; and 6, repeating the steps 3-5 until the power peak factor converges. According to the method, the physical parameters of the reactor core under the bias ring operation condition are calculated, and the control capability of safe operation of the reactor is further improved.

The invention discloses an urban road traffic state intelligent estimation method based on microphysical representation, and the method comprises the steps: constructing a road section boundary condition based on the arrival and departure accumulated flow information of an intersection and a gate, and forming multi-source traffic perception input through combining with road section observation data; constructing a physical-data hybrid driving model, constructing a deep learning model in a data driving branch, learning a mapping relation between road section boundary cumulative flow and a traffic state in a road section, and converting an indistinguishable Newell traffic flow model into a distinguishable computational graph structure through function approximation and structural conversion in a distinguishable physical branch; and constructing a loss function of the hybrid drive model, so that data drive output and a physical model are kept coordinated, and traffic flow parameters are jointly estimated. Through deep embedding of the microphysical calculation graph, the dependence of a pure physical model on an ideal assumed condition is made up, and meanwhile, the problem that a pure data driving method is insufficient in generalization ability in a sensing blind area is solved.

This invention relates to the fields of artificial intelligence and computational materials science, and discloses a method and system for autonomous material discovery based on a large language model. The method includes: the system receiving computational task instructions in natural language form; a cognitive layer calling a large language model agent to process the instructions and outputting structured instructions containing action instructions and parameter sets; a control bridging layer merging and pattern-validating the parameter sets based on preset system parameters, generating file entities and writing them to the processing path; an execution layer starting a background daemon process and triggering atomic movement instructions from the operating system; a computation module parsing the file entities, initializing a graph neural network model, and performing iterative computation; during the computation process, collecting and transmitting runtime status data including a loss function, and having the large language model agent dynamically generate the next round of computational task instructions based on the numerical values. This invention establishes a closed loop of instruction parsing, mutual exclusion scheduling, and physical computation, improving the stability of autonomous material computation.

The application discloses a steel modular building structure performance prediction method and system based on a graph neural network, and belongs to the technical field of physical calculation. In view of the problems that multi-specialty data is split, index calculation relies on manual work, and numerical simulation is time-consuming and difficult to support rapid iteration in the prior art, integrated digital basic JSON data is first constructed based on DXF drawings; then building material cost is automatically calculated, structure performance indexes are extracted, and building cold and heat loads are calculated; finally, a graph convolution network structure proxy model and a heterogeneous graph convolution network energy consumption proxy model are trained by taking the calculated indexes as a supervision signal, the structure topology and the energy consumption heterogeneous relationship are accurately captured through the graph neural network, and rapid and accurate prediction of structure performance and building energy consumption is realized. The application realizes homologous integration of multi-specialty data and automatic calculation of indexes, and provides technical support for efficient design of steel modular building structures.

The invention relates to the technical field of short-circuit current calculation, in particular to a flexible direct current system short-circuit current calculation method based on a physical information neural network, and the method comprises the steps: setting a model input feature vector which is used for representing a system operation state and fault information before a fault, carrying out data preprocessing on the model input feature vector; a hybrid drive calculation model is constructed, the hybrid drive calculation model comprises a physical calculation module and a neural network module, and coupling is carried out based on a preset fusion architecture; adopting a preset composite loss function to perform end-to-end training and optimization on the hybrid drive calculation model; and performing flexible DC system short-circuit current calculation based on the optimized hybrid drive calculation model. The method solves the problems of poor precision, speed and convergence, poor interpretability and weak generalization ability in the prior art.

The invention provides a miscible multiphase fluid state prediction method and device, equipment and a medium, and the method comprises the steps: obtaining the state of a fluid system at the current moment, and obtaining at least one physical parameter used for controlling the dynamic evolution behavior of the fluid; according to the Newton's law of motion, calculating initial predicted values of the position and the speed of the fluid particles at the next moment; utilizing the trained multiphase fluid prediction network to predict the position correction of fluid particles and the volume fraction of each phase at the next moment; and obtaining the position and speed of the fluid particle at the next moment according to the position of the fluid particle at the current moment, the initial predicted value of the position and the position correction amount. Physical calculation and multiphase fluid prediction network correction are combined, and the network only needs to learn the deviation from physical basic motion, so that the learning difficulty is reduced, and the simulation process is more stable; and physical parameters are independently used as an input, so that the universality and the flexibility of the model are greatly enhanced.

The invention discloses a mathematical model-based wave visual simulation method and simulation system. The mathematical model-based wave visual simulation method comprises the following steps of: constructing a consistent wave spectrum model; initializing a wave spectrum model, and calculating and generating a wave amplitude array; acquiring the position, speed and course of the ship, and calculating encounter frequency; a wave height field is calculated and generated, and the height of encountered waves is calculated according to the position of the ship; wave force is calculated, the wave height field is accelerated through a GPU to generate a wave height map, and a wave scene is generated; calculating the position, speed, course and attitude of the ship, and updating the three-dimensional model display of the ship; according to the method, the problem that visual simulation and a dynamic response model are disjointed is effectively solved, the ship motion visual scene is highly consistent with a physical calculation result, immersive ship motion visual scene experience can be provided for a user, real interaction between a ship and waves can be accurately simulated, and the ship motion visual scene experience is improved. The practical value of visual simulation in the fields of ship control system design, seakeeping analysis and sailor safety training is improved.

The invention discloses a quantum field theory high-performance coprocessor based on path integration and a method thereof, and belongs to the field of high-energy physical calculation, particle collision simulation, quantum color dynamics and quantum gravity calculation. Comprising a functional integral discretization engine (FIDE), a Feynman graph automatic generator (FAGG), a Wick contraction accelerator (WCA), a reformer group flow engine (RGFE), a path integral Monte Carlo sampler (PIMCS), a correlation function calculation unit (CFCU), a symmetry breaking detector (SSBD), a vacuum energy extractor (VEE), a Fermi determinant solver (FDS), a topological charge calculator, a circle graph integral optimizer, a Ward identical verifier and a cross-theory mapper. A hardware acceleration interface; according to the invention, an ASIC + GPU + quantum mixed framework is adopted, leading-edge applications such as LHC collision cross section prediction, quark gum plasma simulation, dark substance interaction calculation, early universe phase change research, quantum gravitational non-perturbation effect and the like are supported, and accurate inspection of a particle physical standard model and new physical search beyond the standard model are promoted.

An information processing device is configured so as to execute a first division execution step, a physical computation step, and a second division execution step. The target data is mesh data, polygon data, or CAD data for an object or a space, and the regional division is computation for dividing the target data into a plurality of regions. In the first division execution step, first regional division of the target data is executed. In the physical computation step, physical computation is performed on the target data subjected to the first regional division. In the second division execution step, the result of the physical computation is used as a basis to determine a plurality of flow tubes, flow lines, or flow surfaces. In the second division execution step, second regional division is executed on the basis of the flow tubes, the flow lines, or the flow surfaces.

The invention relates to the technical field of geotechnical engineering, and provides a pile loading-pile foundation dynamic feedback optimization method and system based on settlement data driving. The pile loading-pile foundation dynamic feedback optimization method based on settlement data driving comprises the following steps: constructing a data set based on historical settlement monitoring data; training a settlement prediction model by using the data set to obtain a settlement prediction value corresponding to the field settlement monitoring data; the settlement prediction model is composed of a physical calculation model and a machine learning model, and the settlement prediction value is the sum of a settlement theoretical value calculated by the physical calculation model and a residual value learned by the machine learning model; and a pile foundation scheme meeting the settlement constraint is screened out according to the settlement predicted value, and then the optimal pile foundation scheme is obtained with the shortest construction period and the lowest manufacturing cost as optimization targets. According to the method, an early-stage stacking scheme can be optimized by utilizing prediction data, and the practical applicability of the stacking scheme is improved.

The present application relates to rock material elastic modulus field and Poisson's ratio field prediction technical field, especially to a kind of rock elastic modulus and Poisson's ratio field prediction method based on physical constraint, the method includes constructing rock mechanics sample data set, the data set includes rock elastic modulus field, Poisson's ratio field, strain field, stress field and spatial coordinates, construct physical information neural network, including constructing encoder, decoder and physical calculation layer, the physical calculation layer is used to calculate the strain field of mechanical response;Based on physical information neural network, construct the multiple composite loss function of fusion physical constraint, based on multiple composite loss function, physical information neural network is iteratively trained, and the high-resolution elastic modulus field and Poisson's ratio field of target rock test piece are output, the present application introduces parameter physical constraint in model training process, ensure that the elastic modulus and Poisson's ratio parameter field predicted meet the basic law of rock mechanics, improve the credibility of prediction result.

This application discloses a digital twin simulation method and system for orthopedic medical devices, comprising: constructing a digital twin of the skeleton, reconstructing the three-dimensional anatomical geometry of the skeleton based on the patient's medical imaging data, fusing real-time sensor information and physiological load spectrum, and endowing it with individualized biomechanical properties and dynamic response characteristics; computational-measurement fusion simulation, integrating finite element analysis, multibody dynamics calculation, and machine learning-driven surrogate models, fusing and dynamically calibrating the physical calculation results with the measured data of the implant's in-situ state, realizing real-time simulation and prediction of the mechanical performance of the implanted device under complex physiological environments; and three-dimensional visualization feedback, constructing a virtual assembly environment, dynamically rendering the spatial position and interaction state of the medical device and the skeleton under the drive of the physiological load spectrum, and presenting key biomechanical indicators as an engineering cloud map. This application solves the problem in the prior art of balancing modeling accuracy and computational efficiency in medical device R&D simulation.

An apparatus includes a circuit board configured for attachment to a cover by a first fastener. The circuit board includes a first via sized to receive a distal portion of a first shaft of the first fastener. A first upper portion of the first shaft is to be received in a through-hole in the cover. The apparatus further includes a first conductive trace extending from a first side wall defining the first via to first circuitry. The first conductive trace is configured to carry one or more input signals to the first circuitry indicating whether the distal portion of the first shaft is at least partially disposed in the first via and communicatively coupled to a first conductive element associated with the first via. The first conductive element may include a first conductive plating at least partially covering the first side wall defining the first via.

The invention relates to a remote sensing satellite geometric positioning method and system based on physical constraint and dynamic learning, and belongs to the technical field of space remote sensing and artificial intelligence. The method comprises the following steps: firstly, constructing a strict physical model containing implicit error compensation amount, providing structural constraint and an initial solution by using the physical model, then compensating a complex time-varying residual error which cannot be accurately described by the physical model by using an AI model, and performing dynamic enhancement and parameterization transformation on the physical model through the AI model to obtain a time-varying residual error. The physical model has the evolvable ability and is continuously optimized along with on-orbit operation, a two-way closed-loop collaborative mechanism of the physical model, AI dynamic learning, physical model parameter correction and more accurate physical calculation is formed, and therefore comprehensive breakthrough is achieved in the aspects of precision, efficiency, interpretability and long-term adaptability.

The application provides a kind of low-altitude flight simulation system-oriented abnormal test method and device, belong to computer simulation technical field.The method comprises: in the low-altitude flight simulation process of at least one simulation entity, whenever triggering abnormal injection condition, determine injection instruction, injection instruction carries target abnormal event to be injected;Based on injection instruction, target abnormal event is injected into target engine, target engine is task flow engine, behavior rule engine or physical calculation engine;Based on target abnormal event, carry out collaborative simulation processing between task flow engine, behavior rule engine and physical calculation engine, so that the abnormal effect corresponding to target abnormal event is propagated between each engine, to obtain the causal chain data corresponding to target abnormal event;Based on causal chain data, the abnormal response result of system is quantitatively evaluated.Using the application, the fidelity of abnormal test can be improved.

The invention discloses a power grid operation risk control method for coping with an extreme scene, and relates to the technical field of power system operation decision, and the method comprises the steps: carrying out the clustering processing of all nodes in a power grid through employing a k-means clustering algorithm according to the power fluctuation characteristic measurement value of each node in the power grid, and generating an extreme scene; embedding the physical calculation process of the power grid operation risk in the extreme scene into a training framework of a neural network, performing piecewise linearization representation on the power grid operation risk in the extreme scene by using a large M method, and constructing a scheduling model; and solving the scheduling model by adopting a phototropic growth optimization algorithm to obtain an optimal operation strategy of the power grid when the operation risk of the power grid in the extreme scene is considered. The scheduling model of the power grid considering the power grid operation risk in the extreme scene is constructed, and the anti-risk capability of the power grid operation in the extreme scene is improved.

The invention provides a self-adaptive source iteration acceleration method based on physics-data driving, and belongs to the field of reactor physical computation.The method comprises the steps that an initial threshold value and a convergence standard are set firstly, a steady-state neutron diffusion equation is discretized through a coarse mesh finite difference method, and an equation set is established; whether an acceleration strategy is executed or not is determined by judging the relation between the maximum flux relative error and a threshold value. When the conditions are met, acceleration parameters are calculated, a flux error snapshot matrix is constructed, a residual error ratio is calculated by adopting incremental orthogonalization analysis, and whether flux prediction correction is carried out or not is judged according to the residual error ratio; and after correction, continuously calculating an equation set twice, and adaptively updating a threshold value. And carrying out loop iteration until convergence conditions are met. According to the method, physical constraint and data driving advantages are fused, parameters and strategies are adjusted in a self-adaptive mode, the number of iterations is greatly reduced on the premise that the calculation precision is guaranteed, and the solving efficiency of the steady-state multi-dimensional multi-group neutron diffusion equation is remarkably improved.

Modification of application implementation may include modification, addition, and / or removal of machine-readable instructions. Modification of machine readable instructions prior to run-time may modify implementation of one or more features. Physical computer processor(s) may be configured by computer readable instructions to obtain machine-readable instructions. Machine-readable instructions may, cause a target computing platform to implement an application when executed. Physical computer processor(s) may obtain information regarding implementation of the application by the target computing platform and analyze the machine-readable instructions and / or the information to create one or more rules for modifying application implementation by the computing platform. Physical computer processor(s) may modify the machine-readable instructions based on the rules to add features to and / or remove features from the machine-readable instructions. The system may distribute the modified machine-readable instructions to effectuate the modified machine-readable instructions to be implemented by the target computing platform.

A network-accessible machine learning service is provided herein. For example, the network-accessible machine learning service provider can operate one or more physical computing devices accessible to user devices via a network. These physical computing device(s) can host virtual machine instances that are configured to train machine learning models using training data referenced by a user device. These physical computing device(s) can further host virtual machine instances that are configured to execute trained machine learning models in response to user-provided inputs, generating outputs that are stored and / or transmitted to user devices via the network.