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arXiv 2603.26149math.NAcs.NA
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基于图的无网格多尺度粗空间近似用于两层Schwarz方法

Graph-Based Meshfree Multi-scale Coarse Space Approximation for Two-Level Schwarz Methods

The Chinese University of Hong Kong(香港中文大学);NVIDIA(英伟达)

Yucheng Liu, Tak Shing Au Yeung, Eric T. Chung, Simon See

AI总结:

本文提出一种基于图神经网络的代数粗空间近似方法,用于两层Schwarz方法,以降低计算成本并提高求解效率,同时保持在高对比度和混合边界条件下的鲁棒性。

摘要:

高效模拟高异质多孔介质中的达西流需要在大渗透率对比和混合边界条件下保持鲁棒性的迭代求解器。两阶段重叠Schwarz方法中的频谱粗空间提供了这种鲁棒性,但其实际应用通常受限于由许多局部广义特征值求解主导的昂贵设置阶段。我们提出了一种纯代数的粗空间近似方法,通过在系统矩阵图上操作的图神经网络避免这些重复的局部特征值求解。在分析方面,我们引入了系数加权子空间距离度量来量化近似和目标局部多尺度粗空间之间的差异,并推导出由此得到的预处理算子的条件数界,以该距离为依据。此界提供了有原则的监督训练目标,并将学习误差与求解器性能联系起来。在2D和3D高对比度达西系统中,随着混合边界条件的变化,数值实验表明,所提出的方法显著降低了设置成本并提高了端到端的求解时间,同时在测试的对比度和边界配置下保持了鲁棒的收敛性。

英文摘要:

Efficient simulation of Darcy flow in highly heterogeneous porous media requires iterative solvers that remain robust under large permeability contrasts and mixed boundary conditions. Spectral coarse spaces in two-level overlapping Schwarz methods provide such robustness, but their practical use is often limited by an expensive setup phase dominated by many local generalized eigenvalue solves. We propose a purely algebraic, coarse-space approximation that avoids these repeated local eigensolves by using a graph neural network operating on the system-matrix graph. On the analysis side, we introduce a coefficient-weighted subspace-distance measure to quantify the discrepancy between the approximated and target local multiscale coarse spaces, and we derive a condition-number bound for the resulting preconditioned operator in terms of this distance. This bound yields a principled supervised-training objective and links learning error to solver performance. Numerical experiments on 2D and 3D high-contrast Darcy systems with varying mixed boundary conditions demonstrate that the proposed approach substantially reduces setup cost and improves end-to-end time-to-solution, while preserving robust convergence across the tested contrasts and boundary configurations.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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