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Multi-fidelity bayesian optimization using model-order reduction for viscoplastic structures
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DOI：10.1016/j.finel.2020.103400.png)
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Abstract
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One of the main issues when dealing with the numerical optimization of mechanical structures is the balance between computation time and model accuracy. The work presented herein aims at accelerating global optimization by using the framework of Bayesian optimization on a quantity of interest together with multiple levels of fidelity. These multi-fidelity data are generated from a 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 illustrated on two elasto-viscoplastic test-cases for which significant speedups can be observed.
Keywords:
Bayesian optimization
Multi-fidelity kriging
Reduced-order models
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