{
  "id": "2006.04482",
  "version": "v1",
  "published": "2020-06-08T11:20:26.000Z",
  "updated": "2020-06-08T11:20:26.000Z",
  "title": "Dimensional Reduction of Dynamical Systems by Machine Learning: Automatic Generation of a Macroscopic Model",
  "authors": [
    "Tomoaki Nogawa"
  ],
  "comment": "9 pages, 7 figures",
  "categories": [
    "cond-mat.stat-mech"
  ],
  "abstract": "We propose the framework to generate a phenomenological model that extract the essence of a dynamical system with large degrees of freedom by using machine learning. For a given microscopic dynamical system, we simultaneously seek for the suitable projection to a macroscopic variable, which is supposed to be extensive, and the time proceeding equation that governs them. The utility of this method is demonstrated by the application to the elementary cellular automata.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2020-06-08T11:20:26.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "macroscopic model",
      "machine learning",
      "dimensional reduction",
      "automatic generation",
      "elementary cellular automata"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 9,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}