{
  "id": "1808.03311",
  "version": "v1",
  "published": "2018-08-09T19:21:52.000Z",
  "updated": "2018-08-09T19:21:52.000Z",
  "title": "Model order reduction for parametrized nonlinear hyperbolic problems as an application to Uncertainty Quantification",
  "authors": [
    "R. Crisovan",
    "D. Torlo",
    "R. Abgrall",
    "S. Tokareva"
  ],
  "categories": [
    "math.NA"
  ],
  "abstract": "In this work, we focus on reduced order modeling (ROM) techniques for hyperbolic conservation laws with application in uncertainty quantification (UQ) and in conjunction with the well-known Monte Carlo sampling method. Because we are interested in model order reduction (MOR) techniques for unsteady non-linear hyperbolic systems of conservation laws, which involve moving waves and discontinuities, we explore the parameter-time framework and in the same time we deal with nonlinearities using a POD-EIM-Greedy algorithm \\cite{Drohmann2012}. We provide under some hypothesis an error indicator, which is also an error upper bound for the difference between the high fidelity solution and the reduced one.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2018-08-09T19:21:52.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "model order reduction",
      "parametrized nonlinear hyperbolic problems",
      "uncertainty quantification",
      "application",
      "conservation laws"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}