A fully differentiable GNN-based PDE Solver: With Applications to Poisson and Navier-Stokes Equations
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866911870023106560 |
|---|---|
| author | Li, Tianyu Zou, Yiye Zou, Shufan Chang, Xinghua Zhang, Laiping Deng, Xiaogang |
| author_facet | Li, Tianyu Zou, Yiye Zou, Shufan Chang, Xinghua Zhang, Laiping Deng, Xiaogang |
| contents | In this study, we present a novel computational framework that integrates the finite volume method with graph neural networks to address the challenges in Physics-Informed Neural Networks(PINNs). Our approach leverages the flexibility of graph neural networks to adapt to various types of two-dimensional unstructured grids, enhancing the model's applicability across different physical equations and boundary conditions. The core innovation lies in the development of an unsupervised training algorithm that utilizes GPU parallel computing to implement a fully differentiable finite volume method discretization process. This method includes differentiable integral and gradient reconstruction algorithms, enabling the model to directly solve partial-differential equations(PDEs) during training without the need for pre-computed data. Our results demonstrate the model's superior mesh generalization and its capability to handle multiple boundary conditions simultaneously, significantly boosting its generalization capabilities. The proposed method not only shows potential for extensive applications in CFD but also establishes a new paradigm for integrating traditional numerical methods with deep learning technologies, offering a robust platform for solving complex physical problems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_04466 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A fully differentiable GNN-based PDE Solver: With Applications to Poisson and Navier-Stokes Equations Li, Tianyu Zou, Yiye Zou, Shufan Chang, Xinghua Zhang, Laiping Deng, Xiaogang Fluid Dynamics Mathematical Physics Computational Physics In this study, we present a novel computational framework that integrates the finite volume method with graph neural networks to address the challenges in Physics-Informed Neural Networks(PINNs). Our approach leverages the flexibility of graph neural networks to adapt to various types of two-dimensional unstructured grids, enhancing the model's applicability across different physical equations and boundary conditions. The core innovation lies in the development of an unsupervised training algorithm that utilizes GPU parallel computing to implement a fully differentiable finite volume method discretization process. This method includes differentiable integral and gradient reconstruction algorithms, enabling the model to directly solve partial-differential equations(PDEs) during training without the need for pre-computed data. Our results demonstrate the model's superior mesh generalization and its capability to handle multiple boundary conditions simultaneously, significantly boosting its generalization capabilities. The proposed method not only shows potential for extensive applications in CFD but also establishes a new paradigm for integrating traditional numerical methods with deep learning technologies, offering a robust platform for solving complex physical problems. |
| title | A fully differentiable GNN-based PDE Solver: With Applications to Poisson and Navier-Stokes Equations |
| topic | Fluid Dynamics Mathematical Physics Computational Physics |
| url | https://arxiv.org/abs/2405.04466 |