Surrogate modeling by multifidelity cokriging for the ductile failure of random microstructures

Abstract

A nonparametric surrogate model for ductile failure is developed from simulation results on cells with a random distribution of voids. This model fully takes into account the anisotropy induced by the simulation conditions. The metamodeling strategy uses Gaussian Process Regression coupled with a multifidelity approach involving simulations on a cell with a single void. Through cokriging and metamodel parameter transfer, information can be transferred from the unit cell simulations to the model on random cells. This allows an increased accuracy, for a given computational capacity. Strategies for adaptive experimental design are also investigated.