{
  "id": "1712.07854",
  "version": "v1",
  "published": "2017-12-21T10:06:28.000Z",
  "updated": "2017-12-21T10:06:28.000Z",
  "title": "Prediction of laminar vortex shedding over a cylinder using deep learning",
  "authors": [
    "Sangseung Lee",
    "Donghyun You"
  ],
  "categories": [
    "physics.flu-dyn"
  ],
  "abstract": "Unsteady laminar vortex shedding over a circular cylinder is predicted using a deep learning technique, a generative adversarial network (GAN), with a particular emphasis on elucidating the potential of learning the solution of the Navier-Stokes equations. Numerical simulations at two different Reynolds numbers with different time-step sizes are conducted to produce training datasets of flow field variables. Unsteady flow fields in the future at a Reynolds number which is not in the training datasets are predicted using a GAN. Predicted flow fields are found to qualitatively and quantitatively agree well with flow fields calculated by numerical simulations. The present study suggests that a deep learning technique can be utilized for prediction of laminar wake flow in lieu of solving the Navier-Stokes equations.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2017-12-21T10:06:28.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "laminar vortex shedding",
      "prediction",
      "deep learning technique",
      "navier-stokes equations",
      "reynolds number"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}