Multigrid Methods for a Class of Saddle Point Problems

一类鞍点问题的多重网格方法

• 批准号: 1418934
• 负责人: Long Chen
• 金额: $ 20.5万
• 依托单位: University of California-Irvine
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-08-15 至 2018-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1418934&HistoricalAwards=false
• 关键词: Multigrid; Methods; Class; Saddle; Point

The fast multigrid methods developed and studied in this work are expected to have a broader impact on the numerical solutions of a large class of practical problems. Important applications include: vector (Hodge) Laplacian, Maxwell equations, Stokes equations, Oseen and Navier-Stokes equations, and Magnetohydrodynamics (MHD) etc. MHD, in particular, has important applications in the development of fusion technology and casting processes. In these applications, since no experimentation is nowadays possible, the numerical simulation of the corresponding partial differential equations is indispensable. These simulations are very challenging, requiring large computational resources. The multigrid solvers developed in this project offer the potential for increasingly accurate models to be solved. In addition, our improvements in algorithm developments will have impact on many other areas, such as image processing, and computer graphics.This project is divided into two parts: algorithmic development and theoretical analysis. For the algorithmic development, multigrid solvers will be developed for mixed finite element discretization based on Finite Element Exterior Calculus (FEEC). In our study, effective smoothers, which are the key of multigrid methods, will be developed by using existing effective preconditioners or splitting schemes. One such example is a distributive smoother proposed in this project which is highly related to the well-known projection methods used in computational fluid dynamic. In addition to the algorithmic development, a more completed convergence theory of multigrid methods for saddle point problems will be developed. This theory aims to relax the strong regularity assumption in existing work. Consequently our theory can be applied to more realistic problems especially for solutions with singularities. Our theoretical investigation will also provide insight for the algorithmic development, e.g., the construction of approximated distributive smoothers and Schwarz smoothers, and optimal choice of relaxation parameters used in several smoothers.

本文所开发和研究的快速多重网格法有望对一大类实际问题的数值解产生更广泛的影响。重要的应用包括：矢量(Hodge)拉普拉斯方程、麦克斯韦方程、Stokes方程、Osee和Navier-Stokes方程以及磁流体力学(MHD)等。尤其是磁流体力学，在熔化技术和铸造工艺的发展中具有重要的应用。在这些应用中，由于目前还不可能进行实验，相应的偏微分方程组的数值模拟是必不可少的。这些模拟非常具有挑战性，需要大量的计算资源。在这个项目中开发的多重网格解算器为解决越来越精确的模型提供了可能性。此外，我们在算法开发方面的改进将对许多其他领域产生影响，如图像处理和计算机图形学。本项目分为算法开发和理论分析两部分。在算法开发方面，将开发基于有限元外积分(FEEC)的混合有限元离散的多重网格求解器。在我们的研究中，有效的光滑器是多重网格方法的关键，我们将利用现有的有效预条件或分裂格式来开发有效的光滑器。一个这样的例子是本项目中提出的分布式光滑器，它与计算流体力学中使用的众所周知的投影方法高度相关。除了算法的发展外，还将发展更完整的鞍点问题多重网格法的收敛理论。该理论旨在放松现有工作中的强正则性假设。因此，我们的理论可以应用于更现实的问题，特别是具有奇异性的解。我们的理论研究也将为算法的发展提供启示，例如，构造近似分布光滑器和Schwarz光滑器，以及在几种光滑器中使用的松弛参数的最佳选择。