Application of a deepest‐path algorithm to study the objective function landscape during fitting for the Yeoh and Ogden model

Abstract

In this paper we demonstrate the application of the deepest‐path algorithm for better understanding of objective function landscapes resulting from the optimization process. For this purpose, the sensitivities of the Yeoh and Ogden material model parameters are compared for different load cases. This analysis shows a much higher variation of the material parameters for the Ogden model than for the Yeoh model at approximately constant objective function values. The reasons for this may be local minima or shallow gradients in the objective function landscape. Afterwards, the deepest‐path algorithm is performed between selected designs from the sensitivity analysis. It can be seen that the deepest‐path algorithm provides further information about local minima in the objective function landscape, which are not clear identifiable from a sensitivity analysis.