{
  "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.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2605.00068",
  "author_total": 8,
  "authors": [
    {
      "name": "Ricardo Luna Gutierrez"
    },
    {
      "name": "Sahand Ghorbanpour"
    },
    {
      "name": "Rahman Ejaz"
    },
    {
      "name": "Varchas Gopalaswamy"
    },
    {
      "name": "Riccardo Betti"
    },
    {
      "name": "Vineet Gundecha"
    },
    {
      "name": "Aarne Lees"
    },
    {
      "name": "Soumyendu Sarkar"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/3gjkpe1cjh30b.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2605.00068",
  "fulltext_url": null,
  "id": "3gjkpe1cjh30b",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2605.00068",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.AI"
    ],
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "arxiv",
      "physics.plasm-ph"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2605.00068",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-12",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/3gjkpe1cjh30b.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:21:25Z",
      "native_id": "2605.00068",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2605.00068"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:physics.plasm-ph",
      "subfield": "plasma"
    }
  ],
  "title": "Human-in-the-Loop Meta Bayesian Optimization for Fusion Energy and Scientific Applications",
  "title_text": "Human-in-the-Loop Meta Bayesian Optimization for Fusion Energy and Scientific Applications",
  "type": "preprint",
  "updated_at": "2026-10-30T12:21:25Z",
  "url": "https://research.draconitic.com/paper/3gjkpe1cjh30b",
  "venue": "arXiv",
  "versions": []
}