{
  "id": "1810.11701",
  "version": "v1",
  "published": "2018-10-27T20:37:47.000Z",
  "updated": "2018-10-27T20:37:47.000Z",
  "title": "Hull Form Optimization with Principal Component Analysis and Deep Neural Network",
  "authors": [
    "Dongchi Yu",
    "Lu Wang"
  ],
  "comment": "20 pages",
  "categories": [
    "stat.ML",
    "cs.LG",
    "cs.NE"
  ],
  "abstract": "Designing and modifying complex hull forms for optimal vessel performances have been a major challenge for naval architects. In the present study, Principal Component Analysis (PCA) is introduced to compress the geometric representation of a group of existing vessels, and the resulting principal scores are manipulated to generate a large number of derived hull forms, which are evaluated computationally for their calm-water performances. The results are subsequently used to train a Deep Neural Network (DNN) to accurately establish the relation between different hull forms and their associated performances. Then, based on the fast, parallel DNN-based hull-form evaluation, the large-scale search for optimal hull forms is performed.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2018-10-27T20:37:47.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "deep neural network",
      "principal component analysis",
      "hull form optimization",
      "parallel dnn-based hull-form evaluation",
      "optimal hull forms"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 20,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}