Abstract: A recording medium stores a simulation program for causing a computer to execute a process. The process includes: iterating first analysis processing based on a first generation method for generating a stiffness matrix of an analysis target; calculating an index related to an error of the stiffness matrix after the first analysis processing is iterated; changing the first generation method to a second generation method having higher accuracy based on the index related to the error; and iterating second analysis processing based on the second generation method, wherein the first analysis processing includes generating the stiffness matrix by the first generation method, and calculating a physical quantity related to the target, and the second analysis processing includes generating the stiffness matrix by the second generation method, and calculating the physical quantity related to the target.

Abstract: An information processing apparatus generates a feature vector for each of a plurality of nodes included in an element in mesh data, based on a location of the corresponding node, an elastic modulus at the corresponding node, and stress applied to the corresponding node. The information processing apparatus enters the feature vectors of the nodes to a trained machine learning model, performs a convolutional operation on the feature vectors of the nodes, and estimates an element stiffness matrix indicating stiffness of the element based on a result of the convolutional operation.

Abstract: A computer generates, for combinatorial optimization processing that searches for an optimum value of a solution vector including multiple variables each taking a discrete value based on an evaluation value calculated from the solution vector and a coefficient matrix with rows and columns corresponding to the variables, feature data from the coefficient matrix. The computer generates parameter data representing one candidate value amongst multiple candidate values for a parameter controlling a search method of a solver that executes the combinatorial optimization processing by annealing. The computer calculates a predicted value representing a prediction of the evaluation value corresponding to the one candidate value by inputting the feature data and the parameter data to a trained machine learning model. The computer determines a parameter value to be set for the parameter of the solver by repeating the generation of the parameter data and the calculation of the predicted value.

Abstract: An information processing apparatus stores mesh data including a plurality of nodes and a plurality of edges and boundary condition data indicating force applied to an object represented by the mesh data. The information processing apparatus calculates a stiffness matrix including a plurality of stiffness values corresponding to the plurality of edges and a force vector including a plurality of force values corresponding to the plurality of nodes. The information processing apparatus generates feature data from the stiffness matrix and the force vector. The information processing apparatus infers a plurality of displacement amounts corresponding to the plurality of nodes by performing a convolutional operation on the feature data in accordance with a connection relationship of the plurality of nodes.

Abstract: A memory stores a first trained model for determining features of mesh data, which includes a plurality of nodes and a plurality of edges connecting them, by removing some of the edges from the mesh data. A processor generates, from second mesh data, first mesh data having a smaller number of edges than the second mesh data by use of the first trained model. The processor generates, by running a simulation using the first mesh data, first simulation result data that indicates a physical quantity of an object represented by the first mesh data. The processor infers, from the first mesh data and the first simulation result data and by use of a second trained model, second simulation result data that would be obtained by running the simulation using the second mesh data.

Abstract: A processor acquires mesh data representing a shape of an object with a plurality of nodes and result data representing a physical amount of the object, the physical amount having been calculated based on the mesh data. The processor calculates a first feature amount representing a feature of a positional relationship of the plurality of nodes by performing a topological data analysis on the plurality of nodes included in the mesh data. The processor predicts a physical amount calculated from the shape of the object represented with more nodes than the nodes of the mesh data by entering the first feature amount and a second feature amount based on the physical amount represented by the result data to a learned model.

Abstract: An information processing method includes executing a first process of performing a non-linear analysis by iterating a linear analysis, and executing a second process of predicting a residual threshold to be used for determination of convergence of the linear analysis by a prediction model, based on a residual transition and calculation time for each iteration of the linear analysis obtained for each residual threshold using a plurality of experimental values by the first process. The information processing method further includes performing passage of data between the first process and the second process through an inter-process communication using a shared memory set in a memory.

Abstract: An information processing device includes a memory storing information indicating a virtual address space for data to be processed; and a processor that executes, via the virtual address space, a given process on the data to be processed, monitors access from the processor to multiple monitoring regions among a plurality of regions included in the virtual address space and have been set as targets to be monitored, and executes given control based on an accessed monitoring region among the multiple monitoring regions and for which the access has been detected by the processor.