World Congresses of Structural and Multidisciplinary Optimization

The goal of robust design optimization is to improve the quality of a product or process by minimizing the deteriorating e#ects of variable or uncertain parameters. This robustness can be achieved by di#erent approaches using formulations of statistical decision theory which all require many function evaluations throughout the optimization. In cases where no closed-form descriptions of the objective are available and pointwise solutions are expensive to evaluate, metamodelling techniques based on design of experiments are used to replace the actual numerical analysis codes. In this paper we present a well-suited update criterion for global approximation models which have a local measure for the model variance to find a robust optimal design up to the desired precision both in the objective and the robustness