Improved sensitivity analysis in the inverse identification of the parameters of a nonlinear material model

Abstract

During the inverse identification of the parameters of a nonlinear material model via an optimization algorithm, it is advantageous to utilize sensitivity analysis as a pre-processing tool to decrease the dimensions of the design vector by removing insignificant parameters. As regards the optimization and sensitivity analysis, a crucial aspect consists in the choice of the objective function. It is possible to derive special forms of objective functions for better understanding of the functionality of the given complex material model. The present article discusses three types of Python scripts that facilitate the calculation of different objective functions from the numerically and experimentally obtained load-displacement curves.