Neural network‐based parameter estimation for non‐linear finite element analyses

Abstract

Describes the parameter estimation procedures for the non‐linear finite element analysis using the hierarchical neural network. These procedures can be classified as the neural network based inverse analysis, which has been investigated by the authors. The optimum values of the parameters involved in the non‐linear finite element analysis are generally dependent on the configuration of the analysis model, the initial condition, the boundary condition, etc., and have been determined in a heuristic manner. The procedures to estimate such multiple parameters consist of the following three steps: a set of training data, which is produced over a number of non‐linear finite element computations, is prepared; a neural network is trained using the data set; the neural network is used as a tool for searching the appropriate values of multiple parameters of the non‐linear finite element analysis. The present procedures were tested for the parameter estimation of the augmented Lagrangian method for the steady‐state incompressible viscous flow analysis and the time step evaluation of the pseudo time‐dependent stress analysis for the incompressible inelastic structure.