Schwarz solvers and preconditioners for the closest point method

TL;DR

This paper develops and tests Schwarz domain decomposition solvers and preconditioners for efficiently solving surface PDEs discretized via the closest point method, demonstrating improved convergence and scalability.

Contribution

It introduces Schwarz-based solvers and preconditioners tailored for the CPM discretization of surface PDEs, with strategies for domain partitioning and transmission conditions.

Findings

ORAS outperforms RAS in convergence speed.

Domain decomposition preconditioning accelerates iterative solutions.

Methods scale well with increasing computational processes.

Abstract

The discretization of surface intrinsic elliptic partial differential equations (PDEs) poses interesting challenges not seen in flat space. The discretization of these PDEs typically proceeds by either parametrizing the surface, triangulating the surface, or embedding the surface in a higher dimensional flat space. The closest point method (CPM) is an embedding method that represents surfaces using a function that maps points in the embedding space to their closest points on the surface. In the CPM, this mapping also serves as an extension operator that brings surface intrinsic data onto the embedding space, allowing PDEs to be numerically approximated by standard methods in a narrow tubular neighborhood of the surface. We focus here on numerically approximating the positive Helmholtz equation, $\left(c-\Delta_\mathcal{S}\right)u=f,~c\in\mathbb{R}^+$ by the CPM paired with finite differences. This yields a large, sparse, and non-symmetric system to solve. Herein, we develop restricted additive Schwarz (RAS) and optimized restricted additive Schwarz (ORAS) solvers and preconditioners for this discrete system. In particular, we develop a general strategy for computing overlapping partitions of the computational domain, as well as defining the corresponding Dirichlet and Robin transmission conditions. We demonstrate that the convergence of the ORAS solvers and preconditioners can be improved by using a modified transmission condition where more than two overlapping subdomains meet. Numerical experiments are provided for a variety of analytical and triangulated surfaces. We find that ORAS solvers and preconditioners outperform their RAS counterparts, and that using domain decomposition as a preconditioner gives faster convergence over using it as a solver, as expected. The methods exhibit good parallel scalability over the range of process counts tested.