Research on the applicability of Physics-Informed Neural Networks to two-dimensional shallow water equation

DOI

Bibliographic Information

Other Title
  • 洪水時の外水氾濫を対象としたPhysics-Informed Neural Networksによる浸水把握の試み

Abstract

<p>Physical-Informed Neural Networks (PINNs), which directly approximate the partial differential terms of simultaneous partial differential equations, can achieve higher reproducibility than conventional solution methods that use differences. Furthermore, the solution can be obtained much faster. In this study, we investigated the possibility of applying a two-dimensional shallow water equation for calculating the external water inundation of a flood to actual topographical conditions. Since PINNs approximate the partial differential term as a continuous function, they are generally not good at dealing with complex topography with discontinuous shapes. In this research, the water level distribution of complex topography was reproduced by introducing Positional Encoding to improve expressiveness and mitigating discontinuous topography by adding spatial dimension. Furthermore, the calculation time, which used to take about two days, was shortened to a few minutes.</p>

Journal

Keywords

Details 詳細情報について

  • CRID
    1390298124265459200
  • DOI
    10.11532/jsceiii.4.3_638
  • ISSN
    24359262
  • Text Lang
    ja
  • Data Source
    • JaLC
  • Abstract License Flag
    Disallowed

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