{
  "id": "2211.16190",
  "version": "v1",
  "published": "2022-11-28T16:03:21.000Z",
  "updated": "2022-11-28T16:03:21.000Z",
  "title": "Physics Informed Neural Network for Dynamic Stress Prediction",
  "authors": [
    "Hamed Bolandi",
    "Gautam Sreekumar",
    "Xuyang Li",
    "Nizar Lajnef",
    "Vishnu Naresh Boddeti"
  ],
  "comment": "14 pages, 13 figures",
  "categories": [
    "cs.LG"
  ],
  "abstract": "Structural failures are often caused by catastrophic events such as earthquakes and winds. As a result, it is crucial to predict dynamic stress distributions during highly disruptive events in real time. Currently available high-fidelity methods, such as Finite Element Models (FEMs), suffer from their inherent high complexity. Therefore, to reduce computational cost while maintaining accuracy, a Physics Informed Neural Network (PINN), PINN-Stress model, is proposed to predict the entire sequence of stress distribution based on Finite Element simulations using a partial differential equation (PDE) solver. Using automatic differentiation, we embed a PDE into a deep neural network's loss function to incorporate information from measurements and PDEs. The PINN-Stress model can predict the sequence of stress distribution in almost real-time and can generalize better than the model without PINN.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2022-11-28T16:03:21.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "physics informed neural network",
      "dynamic stress prediction",
      "deep neural networks loss function",
      "predict dynamic stress distributions",
      "finite element"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 14,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}