A Finite Difference Informed Graph Network for Solving Steady-State Incompressible Flows on Block-Structured Grids
<|reference_start|>A Finite Difference Informed Graph Network for Solving Steady-State Incompressible Flows on Block-Structured Grids: Recently, advancements in deep learning have enabled physics-informed neural networks (PINNs) to solve partial differential equations (PDEs). Numerical differentiation (ND) using the finite difference (FD) method is efficient in physics-constrained designs, even in parameterized settings, often employing body-fitted block-structured grids for complex flow cases. However, convolution operators in CNNs for finite differences are typically limited to single-block grids. To address this, we use graphs and graph networks (GNs) to learn flow representations across multi-block structured grids. We propose a graph convolution-based finite difference method (GC-FDM) to train GNs in a physics-constrained manner, enabling differentiable finite difference operations on graph unstructured outputs. Our goal is to solve parametric steady incompressible Navier-Stokes equations for flows around a backward-facing step, a circular cylinder, and double cylinders, using multi-block structured grids. Comparing our method to a CFD solver under various boundary conditions, we demonstrate improved training efficiency and accuracy, achieving a minimum relative error of $10^{-3}$ in velocity field prediction and a 20\% reduction in training cost compared to PINNs.<|reference_end|>
@article{zou2024finite-difference-informed,
    title={Finite-difference-informed graph network for solving steady-state
  incompressible flows on block-structured grids},
    author={Yiye Zou, Tianyu Li, Lin Lu, Jingyu Wang, Shufan Zou, Laiping Zhang,
  Xiaogang Deng},
    journal={arXiv preprint arXiv:2406.10534},
    year={2024},
    doi={10.1063/5.0228104},
    archivePrefix={arXiv},
    eprint={2406.10534},
    primaryClass={cs.LG cs.AI physics.flu-dyn}
}