arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2402.11299math.NAcs.NA
收藏

矩形上泊松方程的拟最优复杂度hp-FEM

Quasi-optimal complexity $hp$-FEM for the Poisson Equation on a rectangle

Kars Knook, Sheehan Olver, Ioannis P. A. Papadopoulos

AI总结:

本文利用hp-FEM离散的特殊稀疏结构,结合反向Cholesky分解和ADI方法,在矩形上以拟最优复杂度求解泊松方程及非线性时间演化偏微分方程,并展示了其作为变系数PDE预条件子的有效性。

摘要:

我们在一维情形下证明,hp-有限元方法(hp-FEM)离散可以以最优复杂度求解,因为该离散具有特殊的稀疏结构,确保反向Cholesky分解(从右下角而不是左上角开始的Cholesky分解)保持稀疏。此外,计算和求逆该分解可以在不同单元之间并行化。通过将这一方法纳入Fortunato和Townsend(2020)风格的交替方向隐式(ADI)方法,我们可以在规定容差内,以拟最优复杂度求解矩形上(屏蔽)泊松方程的hp-FEM离散:$O(N^2 \log N)$次运算,其中$N$是每个维度上的最大总自由度。当与快速Legendre变换结合时,我们还可以以$O(N^2 \log^2 N)$次运算的拟最优复杂度求解非线性时间演化偏微分方程,并在(粘性)Burgers方程上进行了演示。我们还展示了该求解器如何作为变系数偏微分方程的有效预条件子,包括支持奇异性的系数。

英文摘要:

We show, in one dimension, that an $hp$-Finite Element Method ($hp$-FEM) discretisation can be solved in optimal complexity because the discretisation has a special sparsity structure that ensures that the reverse Cholesky factorisation (Cholesky starting from the bottom right instead of the top left) remains sparse. Moreover, computing and inverting the factorisation may parallelise across different elements. By incorporating this approach into an Alternating Direction Implicit (ADI) method à la Fortunato and Townsend (2020) we can solve, within a prescribed tolerance, an $hp$-FEM discretisation of the (screened) Poisson equation on a rectangle with quasi-optimal complexity: $O(N^2 \log N)$ operations where $N$ is the maximal total degrees of freedom in each dimension. When combined with fast Legendre transforms we can also solve nonlinear time-evolution partial differential equations in a quasi-optimal complexity of $O(N^2 \log^2 N)$ operations, which we demonstrate on the (viscid) Burgers' equation. We also demonstrate how the solver can be used as an effective preconditioner for PDEs with variable coefficients, including coefficients that support a singularity.

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。

↑