{
  "abstract": "FlowMLLab is an open-source framework that connects validated fluid-mechanics solvers to scientific machine-learning experiments under a reproducible contract. Its computational fluid dynamics (CFD) pathway generates cavity data, whereas its direct simulation Monte Carlo (DSMC) pathway supplies rarefied-flow evidence with sampling diagnostics. Case-wise partitions prevent leakage, and matched non-neural baselines determine whether learning offers a measurable advantage. Examples include a coordinate network and a proper orthogonal decomposition (POD) deep operator network (DeepONet), together with learned kinetic closures. Python components regenerate every figure, while sixteen tutorials use the same interfaces as the command-line tools. By retaining numerical provenance, blind-case protocols and physical validation targets, FlowMLLab emphasizes traceable evidence rather than black-box curve fitting.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.27975",
  "author_total": 1,
  "authors": [
    {
      "name": "Ehsan Roohi"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/8c9e7f6gk3sq9.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.27975",
  "fulltext_url": null,
  "id": "8c9e7f6gk3sq9",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.27975",
  "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",
      "physics.ed-ph"
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  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2609.27975",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-01",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/8c9e7f6gk3sq9.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:23:56Z",
      "native_id": "2609.27975",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.27975"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:physics.ed-ph",
      "subfield": "general"
    }
  ],
  "title": "FlowMLLab: An open-source framework for reproducible computational-fluid-dynamics and scientific-machine-learning experiments",
  "title_text": "FlowMLLab: An open-source framework for reproducible computational-fluid-dynamics and scientific-machine-learning experiments",
  "type": "preprint",
  "updated_at": "2026-10-30T12:23:56Z",
  "url": "https://research.draconitic.com/paper/8c9e7f6gk3sq9",
  "venue": "arXiv",
  "versions": []
}