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Journal Articles Advanced Modeling and Simulation in Engineering Sciences Year : 2020

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

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Hanane Khatouri
  • Function : Author
Tariq Benamara
  • Function : Author
Piotr Breitkopf
Jean Demange
  • Function : Author
Paul Feliot
  • Function : Author

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.

Dates and versions

hal-03328334 , version 1 (29-08-2021)

Identifiers

Cite

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, 2020, 7 (1), ⟨10.1186/s40323-020-00176-z⟩. ⟨hal-03328334⟩
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