A density-matching approach for optimization under uncertainty

Pranay Seshadri; Paul Constantine; Gianluca Iaccarino; Geoffrey Parks

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arXiv · 1409.7089

A density-matching approach for optimization under uncertainty

Pranay Seshadri·Paul Constantine·Gianluca Iaccarino·Geoffrey Parks

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Abstract

Modern computers enable methods for design optimization that account for uncertainty in the system---so-called optimization under uncertainty. We propose a metric for OUU that measures the distance between a designer-specified probability density function of the system response the target and system response's density function at a given design. We study an OUU formulation that minimizes this distance metric over all designs. We discretize the objective function with numerical quadrature and approximate the response density function with a Gaussian kernel density estimate. We offer heuristics for addressing issues that arise in this formulation, and we apply the approach to a CFD-based airfoil shape optimization problem. We qualitatively compare the density-matching approach to a multi-objective robust design optimization to gain insight into the method.

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Pranay Seshadri, Paul Constantine, Gianluca Iaccarino, Geoffrey Parks. 2014-09-24. A density-matching approach for optimization under uncertainty. https://arxiv.org/abs/1409.7089

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