{
  "id": "2203.11005",
  "version": "v1",
  "published": "2022-03-21T14:21:18.000Z",
  "updated": "2022-03-21T14:21:18.000Z",
  "title": "Machine Learning Algorithms in Design Optimization",
  "authors": [
    "Daniele Peri"
  ],
  "categories": [
    "math.OC"
  ],
  "abstract": "Numerical optimization of complex systems benefits from the technological development of computing platforms in the last twenty years. Unfortunately, this is still not enough, and a large computational time is still necessary when mathematical models that include richer (and therefore more realistic) physical models are adopted. In this paper we show how the combination of optimization and Artificial Intelligence (AI), in particular the Machine Learning algorithms, can help in strongly reducing the overall computational times, making possible the use of complex simulation systems within the optimization cycle. Original approaches are also proposed.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2022-03-21T14:21:18.000Z"
    }
  ],
  "analyses": {
    "subjects": [
      "00A69",
      "46N10",
      "65K05",
      "68T05"
    ],
    "keywords": [
      "machine learning algorithms",
      "design optimization",
      "complex systems benefits",
      "complex simulation systems",
      "overall computational times"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}