Human-in-the-Loop Meta Bayesian Optimization for Fusion Energy and Scientific Applications

Abstract:Inertial Confinement Fusion (ICF) holds transformative promise for sustainable, near-limitless clean energy, yet remains constrained by prohibitively high costs and limited experimental opportunities. This paper presents Human-in-the-Loop Meta Bayesian Optimization (HL-MBO), a framework that integrates expert knowledge with few-shot, uncertainty-aware machine learning to accelerate discovery in data-scarce, high-stakes scientific domains. HL-MBO introduces a meta-learned surrogate model with an expert-informed acquisition function to recommend candidate experiments. To foster trust and enable informed decisions, HL-MBO also provides interpretable explanations of its suggestions. We show HL-MBO outperforms current BO methods on ICF energy yield optimization, as well as benchmarks in molecular optimization and critical temperature maximization for superconducting materials.

Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Plasma Physics (physics.plasm-ph)

arXiv:2605.00068 [cs.LG]

(or arXiv:2605.00068v2 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2605.00068 Focus to learn more arXiv-issued DOI via DataCite

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