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Towards real-time fluid dynamics simulation: a data-driven NN-MPS method and its implementation

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  • Qinghe Yao
  • Zhuolin Wang
  • Yi Zhang
  • Zijie Li
  • Junyang Jiang

Abstract

In this work, we construct a data-driven model to address the computing performance problem of the moving particle semi-implicit method by combining the physics intuition of the method with a machine-learning algorithm. A fully connected artificial neural network is implemented to solve the pressure Poisson equation, which is reformulated as a regression problem. We design context-based feature vectors for particle-based on the Poisson equation. The neural network maintains the original particle method’s accuracy and stability, while drastically accelerates the pressure calculation. It is very suitable for GPU parallelization, edge computing scenarios and real-time simulations.

Suggested Citation

  • Qinghe Yao & Zhuolin Wang & Yi Zhang & Zijie Li & Junyang Jiang, 2023. "Towards real-time fluid dynamics simulation: a data-driven NN-MPS method and its implementation," Mathematical and Computer Modelling of Dynamical Systems, Taylor & Francis Journals, vol. 29(1), pages 95-115, December.
    • Handle: RePEc:taf:nmcmxx:v:29:y:2023:i:1:p:95-115
    DOI: 10.1080/13873954.2023.2184835
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