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