{
  "abstract": "Subgrid parametrizations of vertical mixing in ocean models depend on free coefficients that cannot be measured directly and must be calibrated against high-fidelity references such as large-eddy simulations (LES). Existing approaches return point estimates and leave the associated uncertainty unquantified, a limitation when the inverse problem is ill-posed or when distinct parameter configurations fit the data comparably well. Simulation-based inference (SBI) addresses exactly this: given a prior and access to the simulator, it approximates the full posterior over parameters without requiring a tractable likelihood, at a cost set by the number of simulator evaluations. We apply it to \\texttt{tunax}, a JAX-based single-column ocean model, to calibrate the coefficients of its $k$--$\\varepsilon$ closure. A blockwise PCA summary statistic compresses the simulator output along the depth axis while preserving its forcing--horizon--variable structure, making inference tractable at modest budgets. We compare neural posterior estimation and its sequential variants against a recent training-free approach built on a tabular foundation model. The latter recovers informative posteriors from a few hundred simulator calls, outperforming the trained estimators at every budget considered.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.13242",
  "author_total": 6,
  "authors": [
    {
      "name": "Luben M. C. Cabezas"
    },
    {
      "name": "Sacha Wendling"
    },
    {
      "name": "Aurèle Gallard"
    },
    {
      "name": "Gabriel Mouttapa"
    },
    {
      "name": "Julien Le Sommer"
    },
    {
      "name": "Pedro L. C. Rodrigues"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/9mt459q2yk02t.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.13242",
  "fulltext_url": null,
  "id": "9mt459q2yk02t",
  "issns": [],
  "landing_url": "https://hal.science/hal-05755180v1",
  "language": "en",
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "arxiv",
      "physics.ao-ph"
    ],
    [
      "hal",
      "0.stat"
    ],
    [
      "hal",
      "1.stat.ml"
    ],
    [
      "openalex",
      "1910|Oceanographic and Atmospheric Processes"
    ]
  ],
  "openalex": "W7213262066",
  "pdf_url": "https://hal.science/hal-05755180/document",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-03",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/9mt459q2yk02t.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:36Z",
      "native_id": "hal-05755180",
      "source": "hal",
      "url": "https://hal.science/hal-05755180v1"
    },
    {
      "first_seen": "2026-10-30T12:21:25Z",
      "native_id": "2609.13242",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.13242"
    }
  ],
  "subjects": [
    {
      "field": "earth",
      "origin": "arxiv:physics.ao-ph",
      "subfield": "atmospheric"
    },
    {
      "field": "mathematics",
      "origin": "hal:0.stat",
      "subfield": "statistics"
    }
  ],
  "title": "Calibrating subgrid parametrizations of single-column ocean models via simulation-based inference",
  "title_text": "Calibrating subgrid parametrizations of single-column ocean models via simulation-based inference",
  "type": "preprint",
  "updated_at": "2026-10-30T12:21:25Z",
  "url": "https://research.draconitic.com/paper/9mt459q2yk02t",
  "venue": "HAL",
  "versions": []
}