Multi-Fidelity Adaptive Sampling for Surrogate-Based Optimization and Uncertainty Quantification
Andrea Garbo,
Jigar Parekh,
Tilo Rischmann
et al.
Abstract:Surrogate-based algorithms are indispensable in the aerospace engineering field for reducing the computational cost of optimization and uncertainty quantification analyses, particularly those involving computationally intensive solvers. This paper presents a novel approach for enhancing the efficiency of surrogate-based algorithms through a new multi-fidelity sampling technique. Unlike existing multi-fidelity methods which are based on a single multiplicative acquisition function, the proposed technique decoup… Show more
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