{
  "id": "2304.13125",
  "version": "v1",
  "published": "2023-04-14T16:01:18.000Z",
  "updated": "2023-04-14T16:01:18.000Z",
  "title": "Estimating the master stability function from the time series of one oscillator via machine learning",
  "authors": [
    "Joseph D Hart"
  ],
  "categories": [
    "cond-mat.dis-nn"
  ],
  "abstract": "The master stability function (MSF) yields the stability of the globally synchronized state of a network of identical oscillators in terms of the eigenvalues of the adjacency matrix. In order to compute the MSF, one must have an accurate model of an uncoupled oscillator, but often such a model does not exist. We present a machine learning technique for estimating the MSF given only the time series of a single, uncoupled oscillator. We demonstrate the generality of our technique by considering a variety of coupling configurations of networks consisting of Lorenz oscillators or H{\\'e}non maps.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2023-04-14T16:01:18.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "master stability function",
      "time series",
      "uncoupled oscillator",
      "estimating",
      "adjacency matrix"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}