{
  "abstract": "Machine-learning surrogates accelerate physical simulation, but lower prediction error need not coincide with lower error in physically relevant flow statistics. We examine this question for flow around a NACA4418 airfoil using paired computational-fluid-dynamics simulations and experimental particle-image-velocimetry measurements. A mean-preserving input intervention removes velocity fluctuations from selected regions of observed flow histories. Across four neural operators, removing fluctuations from the most energetic 10% of valid observed cells changes forecasts more than equal-area random removal. Because the masks are not matched for removed fluctuation energy, this contrast measures sensitivity, not independent evidence of physical importance. Separately, a CNO has lower velocity-field error but substantially higher two-component fluctuation-energy error than the reference on both analysis subsets. An output attenuation stress test also demonstrates disagreement between benchmark errors and domain-summed fluctuation energy. These single-benchmark results motivate reporting complementary physical diagnostics alongside aggregate prediction scores; they do not establish counterfactual physical correctness.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.00415",
  "author_total": 2,
  "authors": [
    {
      "name": "Somyajit Chakraborty"
    },
    {
      "name": "Xizhong Chen"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/f508ggrwfvqqy.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.00415",
  "fulltext_url": null,
  "id": "f508ggrwfvqqy",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.00415",
  "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.LG"
    ],
    [
      "arxiv",
      "physics.flu-dyn"
    ],
    [
      "openalex",
      "3109|Model Reduction and Neural Networks"
    ]
  ],
  "openalex": "W7216188456",
  "pdf_url": "https://arxiv.org/pdf/2610.00415",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-01",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/f508ggrwfvqqy.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:26:03Z",
      "native_id": "2610.00415",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.00415"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:physics.flu-dyn",
      "subfield": "fluid-dynamics"
    }
  ],
  "title": "Do Better Scores Mean Better Physics? Physics-Grounded Explanations for Sim2Real Neural Operators",
  "title_text": "Do Better Scores Mean Better Physics? Physics-Grounded Explanations for Sim2Real Neural Operators",
  "type": "preprint",
  "updated_at": "2026-10-30T12:26:03Z",
  "url": "https://research.draconitic.com/paper/f508ggrwfvqqy",
  "venue": "arXiv",
  "versions": []
}