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Constrained multi-fidelity surrogate framework using Bayesian optimization with non-intrusive reduced-order basis

Abstract : Abstract This article addresses the problem of constrained derivative-free optimization in a multi-fidelity (or variable-complexity) framework using Bayesian optimization techniques. It is assumed that the objective and constraints involved in the optimization problem can be evaluated using either an accurate but time-consuming computer program or a fast lower-fidelity one. In this setting, the aim is to solve the optimization problem using as few calls to the high-fidelity program as possible. To this end, it is proposed to use Gaussian process models with trend functions built from the projection of low-fidelity solutions on a reduced-order basis synthesized from scarce high-fidelity snapshots. A study on the ability of such models to accurately represent the objective and the constraints and a comparison of two improvement-based infill strategies are performed on a representative benchmark test case.
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https://hal.utc.fr/hal-03328334
Contributor : Piotr Breitkopf <>
Submitted on : Sunday, August 29, 2021 - 11:43:43 PM
Last modification on : Wednesday, September 8, 2021 - 3:24:19 AM

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Hanane Khatouri, Tariq Benamara, Piotr Breitkopf, Jean Demange, Paul Feliot. Constrained multi-fidelity surrogate framework using Bayesian optimization with non-intrusive reduced-order basis. Advanced Modeling and Simulation in Engineering Sciences, SpringerOpen, 2020, 7 (1), ⟨10.1186/s40323-020-00176-z⟩. ⟨hal-03328334⟩

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