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Generalized information reuse for optimization under uncertainty with non-sample average estimators


Type

Article

Change log

Authors

Jarrett, JP 
Willcox, KE 

Abstract

jats:titleSummary</jats:title>jats:pIn optimization under uncertainty for engineering design, the behavior of the system outputs due to uncertain inputs needs to be quantified at each optimization iteration, but this can be computationally expensive. Multifidelity techniques can significantly reduce the computational cost of Monte Carlo sampling methods for quantifying the effect of uncertain inputs, but existing multifidelity techniques in this context apply only to Monte Carlo estimators that can be expressed as a sample average, such as estimators of statistical moments. Information reuse is a particular multifidelity method that treats previous optimization iterations as lower fidelity models. This work generalizes information reuse to be applicable to quantities whose estimators are not sample averages. The extension makes use of bootstrapping to estimate the error of estimators and the covariance between estimators at different fidelities. Specifically, the horsetail matching metric and quantile function are considered as quantities whose estimators are not sample averages. In an optimization under uncertainty for an acoustic horn design problem, generalized information reuse demonstrated computational savings of over 60% compared with regular Monte Carlo sampling.</jats:p>

Description

Keywords

multifidelity, optimization, optimization under uncertainty, probabilistic methods, stochastic problems

Journal Title

International Journal for Numerical Methods in Engineering

Conference Name

Journal ISSN

0029-5981
1097-0207

Volume Title

115

Publisher

Wiley
Sponsorship
EPSRC (1476418)
Engineering and Physical Sciences Research Council (EP/L504920/1)