A stochastic expansion-based approach for design under uncertainty
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An approach for robust design based on stochastic expansions is investigated. The research consists of two parts : 1) stochastic expansions for uncertainty propagation and 2) adaptive sampling for Pareto front approximation. For the first part, a strategy based on the generalized polynomial chaos (gPC) expansion method is developed. Second, in order to alleviate the computational cost of approximating the Pareto front, two strategies based on adaptive sampling for multi-objective problems are presented. The first one is based on the two aforementioned methods, whereas the second one considers, in addition, two levels of fidelity of the uncertainty propagation method.