| Autor: |
Hanane Khatouri, Tariq Benamara, Piotr Breitkopf, Jean Demange, Paul Feliot |
| Jazyk: |
angličtina |
| Rok vydání: |
2020 |
| Předmět: |
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| Zdroj: |
Advanced Modeling and Simulation in Engineering Sciences, Vol 7, Iss 1, Pp 1-20 (2020) |
| Druh dokumentu: |
article |
| ISSN: |
2213-7467 |
| DOI: |
10.1186/s40323-020-00176-z |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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