{
  "id": "2002.00790",
  "version": "v1",
  "published": "2020-01-30T23:41:37.000Z",
  "updated": "2020-01-30T23:41:37.000Z",
  "title": "Data-Driven Discovery of Coarse-Grained Equations",
  "authors": [
    "Joseph Bakarji",
    "Daniel M. Tartakovsky"
  ],
  "categories": [
    "stat.ML",
    "cs.LG",
    "cs.NA",
    "math.NA"
  ],
  "abstract": "A general method for learning probability density function (PDF) equations based on Monte Carlo simulations of random fields is proposed. Sparse linear regression is used to discover the relevant terms of a partial differential equation of the distribution. The various properties of PDF equations, like smoothness and conservation, makes them very well adapted to equation learning methods. The results show a promising direction for data-driven discovery of coarse-grained equations in general.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2020-01-30T23:41:37.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "data-driven discovery",
      "coarse-grained equations",
      "sparse linear regression",
      "monte carlo simulations",
      "learning probability density function"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}