{
  "abstract": "Finding optimal transport protocols for underdamped stochastic processes is a difficult yet important problem in control theory. In this paper we develop a direct method, based on machine learning, to find the optimal solution of the problem in the presence of non-linear boundary conditions, including those modelling the Landauer bit-erasure problem. The dynamics is steered from a single-peak to a double-peak distribution in position space, by only acting on a controllable external potential. The problem is highly non-linear, and analytical solutions are not available in general. We benchmark our numerical results with a perturbative expansion, which is expected to hold in a suitable overdamped limit.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.41599",
  "author_total": 4,
  "authors": [
    {
      "name": "Julia Sanders"
    },
    {
      "name": "Dario Lucente"
    },
    {
      "name": "Marco Baldovin"
    },
    {
      "name": "Paolo Muratore-Ginanneschi"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/ee6vw5kt6767k.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.41599",
  "fulltext_url": null,
  "id": "ee6vw5kt6767k",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.41599",
  "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",
      "cond-mat.stat-mech"
    ],
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "arxiv",
      "math.OC"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2610.41599",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-30",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/ee6vw5kt6767k.ris",
  "sources": [
    {
      "first_seen": "2026-11-02T12:21:15Z",
      "native_id": "2610.41599",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.41599"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:math.OC",
      "subfield": "optimisation-control"
    },
    {
      "field": "physics",
      "origin": "arxiv:cond-mat.stat-mech",
      "subfield": "statistical-nonlinear"
    }
  ],
  "title": "Underdamped Stochastic Optimal Transport with Landauer boundary conditions: machine learning and analytical solution",
  "title_text": "Underdamped Stochastic Optimal Transport with Landauer boundary conditions: machine learning and analytical solution",
  "type": "preprint",
  "updated_at": "2026-11-04T15:20:52Z",
  "url": "https://research.draconitic.com/paper/ee6vw5kt6767k",
  "venue": "arXiv",
  "versions": []
}