---
title: "Human-in-the-Loop Meta Bayesian Optimization for Fusion Energy and Scientific Applications"
authors:
  - "Ricardo Luna Gutierrez"
  - "Sahand Ghorbanpour"
  - "Rahman Ejaz"
  - "Varchas Gopalaswamy"
  - "Riccardo Betti"
  - "Vineet Gundecha"
  - "Aarne Lees"
  - "Soumyendu Sarkar"
doi: "10.24963/ijcai.2026/815"
url: "https://ijcai.org/proceedings/2026/815"
pdf: "https://ijcai.org/proceedings/2026/0815.pdf"
venue: "Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence"
year: 2026
pages: "7328-7336"
track: "AI and Social Good"
keywords:
  - "Machine Learning: Machine Learning"
  - "Multidisciplinary Topics and Applications: Multidisciplinary Topics and Applications"
---

# 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

*Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence*, pages 7328-7336. DOI: [10.24963/ijcai.2026/815](https://doi.org/10.24963/ijcai.2026/815)

## 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 that HL-MBO outperforms current BO methods for ICF energy yield optimization, as well as benchmarks in molecular optimization and critical-temperature maximization for superconducting materials.