使用图形神经网络实现不连续-加勒金有限元素方法,并应用于扩散方程.
Linfeng Li1, Jiansheng Xiang1, Boyang Chen1
1Department of Earth Science and Engineering, Imperial College London, Prince Consort Road, London SW7 2BP, UK.
概括
本研究介绍了一种新的图形神经网络方法,用于使用有限元法 (FEM) 在非结构化的网格上解决部分微分方程. 这种基于机器学习的解决方案实现了高效的计算和理论融合率.
科学领域:
- 计算科学 计算科学
- 数字分析 数字分析
- 机器学习 机器学习
背景情况:
- 机器学习 (ML) 的进步引发了学术界和工业界的兴趣.
- 现有的部分微分方程 (PDEs) 的ML解决方案通常依赖于结构化网格,限制了具有复杂几何形状的应用.
- 非结构化的网格对于复杂的几何问题至关重要.
研究的目的:
- 使用图形神经网络 (GNN) 实现一个非结构化的网格有限元法 (FEM) 解决器.
- 为了弥合ML能力和科学计算中非结构化的网格解决器的需求之间的差距.
主要方法:
- 拟议的FEM解决器使用图形神经网络进行计算密集型算法.
- 它采用了不连续的加勒金配方,并采用了内部惩罚方法来进行空间分离.
- 采用U-Net架构调整的多网格预先条件的克里洛夫解决器作为线性解决器.
主要成果:
- 解决者证明了扩散问题的 (p+1) 顺序的理论收率.
- 在GPU上,它在矩阵操作员评估中实现了6.8MDOF/s的吞吐量.
- 基于GNN的FEM解决方案显示了与优化实现相比有希望的性能.
结论:
- 本文介绍了第一个基于GNN的非结构化网状FEM解决器的实现.
- 该方法可以适应各种PDEs和计算平台 (CPU,GPU).
- 这种方法为利用ML在复杂的科学计算问题上提供了一条可行的途径.
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