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• W2954032455 abstract "Summary This paper addresses the challenge of design optimization under uncertainty when the designer only has limited data to characterize uncertain variables. We demonstrate that the error incurred when estimating a probability distribution from limited data affects the out‐of‐sample performance (ie, performance under the true distribution) of optimized designs. We demonstrate how this can be mitigated by reformulating the engineering design problem as a distributionally robust optimization (DRO) problem. We present computationally efficient algorithms for solving the resulting DRO problem. The performance of the DRO approach is explored in a practical setting by applying it to an acoustic horn design problem. The DRO approach is compared against traditional approaches to optimization under uncertainty, namely, sample‐average approximation and multiobjective optimization incorporating a risk reduction objective. In contrast with the multiobjective approach, the proposed DRO approach does not use an explicit risk reduction objective but rather specifies a so‐called ambiguity set of possible distributions and optimizes against the worst‐case distribution in this set. Our results show that the DRO designs, in some cases, significantly outperform those designs found using the sample‐average or the multiobjective approach." @default.
• W2954032455 created "2019-07-12" @default.
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• W2954032455 date "2019-07-28" @default.
• W2954032455 modified "2023-09-28" @default.
• W2954032455 title "Distributionally robust optimization for engineering design under uncertainty" @default.
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• W2954032455 doi "https://doi.org/10.1002/nme.6160" @default.
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