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Constrained multi-fidelity surrogate framework using Bayesian optimization with non-intrusive reduced-order basis
Advanced Modeling and Simulation in Engineering Sciences
AbstractThis 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.doi:10.1186/s40323-020-00176-z fatcat:flwmfgmkqrfdfah2rsd3x5k6bu