{
  "abstract": "Modern design optimization frameworks aim first and foremost for models with the most accurate predictions without balancing computational overhead. It remains a reason why scaled architecture and multidisciplinary design optimization problems are difficult to address, even with sample-efficient Bayesian optimizers. In this paper, a metric quantifying the computational energy footprint is introduced within a Bayesian optimization framework to guide the parameter setting of a model towards configurations that balance both performance and frugality. The computer experiments highlighted existing tradeoffs between optimum convergence and the underlying energy footprint, and sometimes resulted in both a better-found optimum and lower energy consumption.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": "Journal reference: G. Plat, P. Saves, N. Bartoli, T. Lefebvre, J. Morlier, Energy-aware frugal Bayesian optimization, EngOpt, 2026, Volume 422, 2026, pp 02009",
  "arxiv": "2609.31638",
  "author_total": 5,
  "authors": [
    {
      "name": "Gaston Plat"
    },
    {
      "name": "Paul Saves"
    },
    {
      "name": "Nathalie Bartoli"
    },
    {
      "name": "Thierry Lefebvre"
    },
    {
      "name": "Joseph Morlier"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/61z7xfkvsm01g.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.31638",
  "fulltext_url": null,
  "id": "61z7xfkvsm01g",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.31638",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.AI"
    ],
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "arxiv",
      "math.OC"
    ],
    [
      "arxiv",
      "physics.class-ph"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2609.31638",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-11",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/61z7xfkvsm01g.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:25:12Z",
      "native_id": "2609.31638",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.31638"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:math.OC",
      "subfield": "optimisation-control"
    },
    {
      "field": "physics",
      "origin": "arxiv:physics.class-ph",
      "subfield": "general"
    }
  ],
  "title": "Energy-aware frugal Bayesian optimization",
  "title_text": "Energy-aware frugal Bayesian optimization",
  "type": "preprint",
  "updated_at": "2026-10-30T12:25:17Z",
  "url": "https://research.draconitic.com/paper/61z7xfkvsm01g",
  "venue": "arXiv",
  "versions": [
    {
      "doi": "10.1051/matecconf/202642202009",
      "pid": null,
      "relation": "published-as",
      "title": null,
      "venue": null
    }
  ]
}