Topology optimization under uncertainty

Abstract

Considering stochastic variations in material parameters, geometry, and boundary conditions is for the majority of engineering design problems of pivotal importance, in order to obtain robust and reliable designs. While design optimization under uncertainty has matured for sizing and shape optimization over the past two decades, accounting for stochastic variations in topology optimization is still in its infancy. This lecture will introduce basic approaches to include uncertainty models and predictions into the topology optimization process.