{"type":"ScholarlyArticle","title":"Human-in-the-Loop Meta Bayesian Optimization for Fusion Energy and Scientific Applications","authors":[{"name":"Ricardo Luna Gutierrez","slug":"ricardo-luna-gutierrez","given":"Ricardo","family":"Luna Gutierrez","affiliation":""},{"name":"Sahand Ghorbanpour","slug":"sahand-ghorbanpour","given":"Sahand","family":"Ghorbanpour","affiliation":""},{"name":"Rahman Ejaz","slug":"rahman-ejaz","given":"Rahman","family":"Ejaz","affiliation":""},{"name":"Varchas Gopalaswamy","slug":"varchas-gopalaswamy","given":"Varchas","family":"Gopalaswamy","affiliation":""},{"name":"Riccardo Betti","slug":"riccardo-betti","given":"Riccardo","family":"Betti","affiliation":""},{"name":"Vineet Gundecha","slug":"vineet-gundecha","given":"Vineet","family":"Gundecha","affiliation":""},{"name":"Aarne Lees","slug":"aarne-lees","given":"Aarne","family":"Lees","affiliation":""},{"name":"Soumyendu Sarkar","slug":"soumyendu-sarkar","given":"Soumyendu","family":"Sarkar","affiliation":""}],"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.","doi":"10.24963/ijcai.2026/815","url":"https://ijcai.org/proceedings/2026/815","pdf":"https://ijcai.org/proceedings/2026/0815.pdf","markdown":"https://ijcai.org/proceedings/2026/815.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/815","volume":{"slug":"2026","title":"Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2026","url":"https://ijcai.org/proceedings/2026/","year":2026,"location":"Bremen, Germany","dates":"15-21 August 2026","editors":"Diego Calvanese","isbn":"978-1-956792-09-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2026"},"section":"AI and Social Good","track":"AI and Social Good","number":815,"pages":{"first":7328,"last":7336},"keywords":["Machine Learning: Machine Learning","Multidisciplinary Topics and Applications: Multidisciplinary Topics and Applications"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}