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arXiv · 2605.00068

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

Ricardo Luna Gutierrez·Sahand Ghorbanpour·Rahman Ejaz·Varchas Gopalaswamy·Riccardo Betti·Vineet Gundecha·Aarne Lees·Soumyendu Sarkar
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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.

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Ricardo Luna Gutierrez, Sahand Ghorbanpour, Rahman Ejaz, Varchas Gopalaswamy, Riccardo Betti, Vineet Gundecha, Aarne Lees, Soumyendu Sarkar. 2026-09-12. Human-in-the-Loop Meta Bayesian Optimization for Fusion Energy and Scientific Applications. https://arxiv.org/abs/2605.00068

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