A Space-Time Multigrid Method for Space-Time Finite Element Discretizations of Parabolic and Hyperbolic PDEs

Nils Margenberg, Peter Munch
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Abstract

We present a space-time multigrid method based on tensor-product space-time finite element discretizations. The method is facilitated by the matrix-free capabilities of the {\ttfamily deal.II} library. It addresses both high-order continuous and discontinuous variational time discretizations with spatial finite element discretizations. The effectiveness of multigrid methods in large-scale stationary problems is well established. However, their application in the space-time context poses significant challenges, mainly due to the construction of suitable smoothers. To address these challenges, we develop a space-time cell-wise additive Schwarz smoother and demonstrate its effectiveness on the heat and acoustic wave equations. The matrix-free framework of the {\ttfamily deal.II} library supports various multigrid strategies, including $h$-, $p$-, and $hp$-refinement across spatial and temporal dimensions. Extensive empirical evidence, provided through scaling and convergence tests on high-performance computing platforms, demonstrate high performance on perturbed meshes and problems with heterogeneous and discontinuous coefficients. Throughputs of over a billion degrees of freedom per second are achieved on problems with more than a trillion global degrees of freedom. The results prove that the space-time multigrid method can effectively solve complex problems in high-fidelity simulations and show great potential for use in coupled problems.
用于抛物线和双曲型 PDE 的时空有限元离散化的时空多网格法
我们提出了一种基于张量乘积时空有限元离散的时空多网格方法。该方法得益于{\ttfamily deal.II}库的矩阵自由能力。它同时解决了高阶连续和非连续变分时间离散与空间有限元离散的问题。多网格方法在大规模静态问题中的有效性已得到公认。然而,多网格方法在时空背景下的应用却面临着巨大挑战,这主要是由于需要构建合适的平滑器。为了应对这些挑战,我们开发了时空单元加性施瓦茨平滑器,并在热方程和声波方程中证明了它的有效性。{\ttfamily deal.II}库的无矩阵框架工作支持各种多网格策略,包括跨空间和时间维度的$h$-、$p$-和$hp$-精简。在高性能计算平台上进行的扩展和收敛测试提供了大量经验证据,证明在扰动网格以及具有异构和非连续系数的问题上具有很高的性能。在全局自由度超过一万亿的问题上,每秒的吞吐量超过十亿个自由度。结果证明,时空多网格方法可以有效地解决高保真模拟中的复杂问题,并在耦合问题中显示出巨大的应用潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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