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arXiv 1704.04549math.NAcs.NA
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近似张量积预处理器用于非常高的阶不连续Galerkin方法

Approximate tensor-product preconditioners for very high order discontinuous Galerkin methods

Division of Applied Mathematics, Brown University(布朗大学应用数学部);Department of Mathematics, University of California, Berkeley(加州大学伯克利分校数学系)

Will Pazner, Per-Olof Persson

AI总结:

本文提出了一种基于张量积的预处理器,用于高阶不连续Galerkin方法,通过SVD实现低存储和计算复杂度,显著提升高阶多项式下的求解效率。

摘要:

本文开发了一种新的基于张量积的预处理器,用于多项式阶数高于通常使用的不连续Galerkin方法。该预处理器使用自动纯代数方法,通过Kronecker积近似精确的块Jacobi预处理器。传统基于矩阵的预处理器需要O(p^{2d})存储和O(p^{3d})计算工作,其中p是基多项式阶数,d是空间维度。我们的基于SVD的张量积预处理器需要O(p^{d+1})存储,二维空间中O(p^{d+1})工作,三维空间中O(p^{d+2})工作。结合无矩阵牛顿-克雷洛夫求解器,这些预处理器允许在2D中每个自由度以线性时间求解DG系统,并将3D计算复杂度从O(p^9)降低到O(p^5)。在2D和3D中,使用多项式阶数高达p=15的Advection和Euler方程进行了数值实验。对于许多测试案例,预处理器的迭代次数与精确块Jacobi预处理器相似,而在高多项式阶数p下性能显著提升。

英文摘要:

In this paper, we develop a new tensor-product based preconditioner for discontinuous Galerkin methods with polynomial degrees higher than those typically employed. This preconditioner uses an automatic, purely algebraic method to approximate the exact block Jacobi preconditioner by Kronecker products of several small, one-dimensional matrices. Traditional matrix-based preconditioners require $\mathcal{O}(p^{2d})$ storage and $\mathcal{O}(p^{3d})$ computational work, where $p$ is the degree of basis polynomials used, and $d$ is the spatial dimension. Our SVD-based tensor-product preconditioner requires $\mathcal{O}(p^{d+1})$ storage, $\mathcal{O}(p^{d+1})$ work in two spatial dimensions, and $\mathcal{O}(p^{d+2})$ work in three spatial dimensions. Combined with a matrix-free Newton-Krylov solver, these preconditioners allow for the solution of DG systems in linear time in $p$ per degree of freedom in 2D, and reduce the computational complexity from $\mathcal{O}(p^9)$ to $\mathcal{O}(p^5)$ in 3D. Numerical results are shown in 2D and 3D for the advection and Euler equations, using polynomials of degree up to $p=15$. For many test cases, the preconditioner results in similar iteration counts when compared with the exact block Jacobi preconditioner, and performance is significantly improved for high polynomial degrees $p$.

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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