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arXiv 2005.01275math.NAcs.NA
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基于局部谱粗化的非线性多重网格求解非均匀扩散问题

Nonlinear multigrid based on local spectral coarsening for heterogeneous diffusion problems

Chak Shing Lee, François Hamon, Nicola Castelletto, Panayot S. Vassilevski, Joshua White

摘要:

本文发展了一种非线性多重网格方法来求解一般非结构网格上的扩散问题。多重网格层次结构的代数构造使用聚合度的自由和谱分解的参考线性算子与聚合。为了快速收敛,重要的是所得到的粗糙空间具有良好的近似性质。在我们的方法中,近似质量可以直接提高包括更多的光谱自由度的粗化过程中。此外,通过利用局部粗化和分段常数近似时,评估的非线性组件,组装和解决的粗级问题,而无需重新访问精细的水平,一个基本要素的多重网格算法,以实现最佳的可扩展性。数值例子比较相对性能的建议的非线性多重网格求解器与标准的单级方法-皮卡德和牛顿法-。结果表明,所提出的求解器始终优于单级方法,无论是在效率和鲁棒性。

英文摘要:

This work develops a nonlinear multigrid method for diffusion problems discretized by cell-centered finite volume methods on general unstructured grids. The multigrid hierarchy is constructed algebraically using aggregation of degrees of freedom and spectral decomposition of reference linear operators associated with the aggregates. For rapid convergence, it is important that the resulting coarse spaces have good approximation properties. In our approach, the approximation quality can be directly improved by including more spectral degrees of freedom in the coarsening process. Further, by exploiting local coarsening and a piecewise-constant approximation when evaluating the nonlinear component, the coarse level problems are assembled and solved without ever re-visiting the fine level, an essential element for multigrid algorithms to achieve optimal scalability. Numerical examples comparing relative performance of the proposed nonlinear multigrid solvers with standard single-level approaches -- Picard's and Newton's methods -- are presented. Results show that the proposed solver consistently outperforms the single-level methods, both in efficiency and robustness.

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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