A deterministic approximation method in shape optimization under random uncertainties

Allaire, Grégoire; Dapogny, Charles

The SMAI Journal of computational mathematics, Tome 1 (2015), pp. 83-143.

This paper is concerned with the treatment of uncertainties in shape optimization. We consider uncertainties in the loadings, the material properties, the geometry and the vibration frequency, both in the parametric and geometric optimization setting. We minimize objective functions which are mean values, variances or failure probabilities of standard cost functions under random uncertainties. By assuming that the uncertainties are small and generated by a finite number $N$ of random variables, and using first- or second-order Taylor expansions, we propose a deterministic approach to optimize approximate objective functions. The computational cost is similar to that of a multiple load problems where the number of loads is $N$. We demonstrate the effectiveness of our approach on various parametric and geometric optimization problems in two space dimensions.

Supplementary Materials:
Supplementary material for this article is supplied as a separate file:

• Allaire-Dapogny-supp.pdf

Classification : 65C20, 65K10, 93C95
Mots clés : Shape optimization, random uncertainties, Level Set method

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