{
  "annotation_id": "5a03e106-54ac-472c-bfca-fe479a238934",
  "date_created": "2026-03-02T18:01:11.020000Z",
  "date_modified": "2026-03-02T18:01:11.020000Z",
  "file_hash": "f39381ee9e840b8435ff28ff33dd7c244e7506efd065817daeccf4ac6da6396f",
  "private": false,
  "record": {
    "abstract": "The tools of optimal estimation are applied to the study of subgrid models\nfor Large-Eddy Simulation of turbulence. The concept of optimal estimator is\nintroduced and its properties are analyzed in the context of applications to a\npriori tests of subgrid models. Attention is focused on the Cook and Riley\nmodel in the case of a scalar field in isotropic turbulence. Using DNS data,\nthe relevance of the beta assumption is estimated by computing (i) generalized\noptimal estimators and (ii) the error brought by this assumption alone. Optimal\nestimators are computed for the subgrid variance using various sets of\nvariables and various techniques (histograms and neural networks). It is shown\nthat optimal estimators allow a thorough exploration of models. Neural networks\nare proved to be relevant and very efficient in this framework, and further\nusages are suggested.",
    "arxiv_id": "physics/0606053",
    "authors": [
      "Antoine Moreau",
      "Olivier Teytaud",
      "Jean-Pierre Bertoglio"
    ],
    "categories": [
      "physics.class-ph",
      "cs.NE"
    ],
    "doi": "10.1063/1.2357974",
    "journal_ref": "Physics of Fluids 18 (04/10/2006) 105101",
    "title": "Optimal estimation for Large-Eddy Simulation of turbulence and   application to the analysis of subgrid models",
    "url": "https://arxiv.org/abs/physics/0606053"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "c81833a2-433c-4bb6-b845-2d711f55e818",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}