10 results about "Physical computation" patented technology

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.

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.

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 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 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.