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

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

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 a...

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Bibliographic Details
Main Authors: Hanane Khatouri, Tariq Benamara, Piotr Breitkopf, Jean Demange, Paul Feliot
Format: Article
Language:English
Published: SpringerOpen 2020-11-01
Series:Advanced Modeling and Simulation in Engineering Sciences
Subjects:
Multi-fidelity
Variable complexity
Black-box optimization
Non-intrusive reduced basis
Bayesian optimization
Online Access:http://link.springer.com/article/10.1186/s40323-020-00176-z

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http://link.springer.com/article/10.1186/s40323-020-00176-z

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