Dealing with Model Errors in Approximation Model-Based Optimization

Abstract

Approximation models are often used in place of complex analysis code during optimization. Uncertainties in the model parameters could lead to inaccuracies in the process. This paper presents a Bayesian approach for finding an optimum that is robust to the uncertainty in model parameters. We test the proposed methodology with two model types applied to standard electromagnetics benchmark problems