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
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 combines 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. By embedding human expertise into the optimization loop, HL-MBO opens a practical and scalable path to advance socially impactful scientific research.
BibTeX
@inproceedings{ijcai2026_humanintheloopme,
title = {Human-in-the-Loop Meta Bayesian Optimization for Fusion Energy and Scientific Applications},
author = {Ricardo Luna Gutierrez and Sahand Ghorbanpour and Rahman Ejaz and Varchas Gopalaswamy and Riccardo Betti and Vineet Gundecha and Aarne Lees and Soumyendu Sarkar},
booktitle = {IJCAI 2026},
year = {2026}
}