Pré-Publication, Document De Travail Année : 2019

Multi-fidelity bayesian optimization using model-order reduction for viscoplastic structures

David Néron
Christian Rey

Résumé

The numerical optimization of a mechanical part requires a balance between computation time and model accuracy. The work presented herein aims at accelerate global optimization problem by using the framework of Bayesian optimization on a quantity of interest with multiple levels of fidelity. These multi-fidelity data are generated from a quality-driven model-order reduction framework: the LATIN Proper Generalized Decomposition. Within this framework, a reduced-order basis is generated on-the-fly and re-exploited to reduce the computational cost of observations. This strategy is tested on two elasto-viscoplastic test cases: a rocket damper and an aircraft blade and gives significant speedups.
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Dates et versions

hal-02396283 , version 1 (05-12-2019)

Identifiants

  • HAL Id : hal-02396283 , version 1

Citer

Stéphane Nachar, Pierre-Alain Boucard, David Néron, Christian Rey. Multi-fidelity bayesian optimization using model-order reduction for viscoplastic structures. 2019. ⟨hal-02396283⟩
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