Parallel Search of Analysis Parameters for Nonlinear Finite Element Methods Using Bayesian Optimization Reflecting Analysis Progress

Abstract

In finite element analysis (FEA) that takes into account nonlinearities such as large deformation and elastic-plasticity, a solution is often not obtained depending on the input parameters. Therefore, we proposed the parallel search method for analyzable input parameters in nonlinear FEA using Bayesian optimization. However, an issue remained that the search would be adversely affected as the number of parallel processes increased. Therefore, in this study, we proposed a parallel search method that reflects the progress of FEA being executed in each processors. The proposed method was applied to a simple rectangular body torsion analysis and achieved a 24% computational time reduction compared to conventional Bayesian optimization in 120 parallel executions.