Robust Active Learning for More Efficient and Reliable Paths to Optimal Design

Abstract:Simulation-driven design optimization requires active learning strategies that efficiently combine models with different computational costs and levels of fidelity. We introduce Multifidelity Cost-Aware Screening (MF-CAS), which enables the joint selection of sampling location and fidelity using a surrogate that provides decomposed uncertainty intervals rather than a complete predictive probability distribution. MF-CAS exploits the contribution of each fidelity to the high-fidelity uncertainty together with its computational cost to determine whether a candidate should first be evaluated at low fidelity or directly progressed towards high fidelity. The method is assessed on the constrained 14-dimensional hydrodynamic optimization of the DTMB 5415 hull under a deliberately limited computational budget. MF-CAS achieves an approximately 9.0% resistance reduction, compared with 6.7% for the best baseline optimisation method, demonstrating that incorporating fidelity-resolved uncertainty directly into the joint location-fidelity decision can substantially improve multifidelity active learning efficiency.

Computational Engineering, Finance, and Science (cs.CE); Machine Learning (cs.LG)

arXiv:2610.38582 [cs.CE]

(or arXiv:2610.38582v1 [cs.CE] for this version)

https://doi.org/10.48550/arXiv.2610.38582 Focus to learn more arXiv-issued DOI via DataCite

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