Monolithic Multigrid Preconditioners for High-Order Discretizations of Stokes Equations
Alexey Voronin, Graham Harper, Scott MacLachlan, Luke N. Olson,, Raymond S. Tuminaro

TL;DR
This paper presents a monolithic $ph$MG multigrid framework for high-order discretizations of Stokes equations, improving efficiency by coarsening in both approximation order and mesh resolution, outperforming traditional methods in many scenarios.
Contribution
It introduces a novel $ph$MG multigrid approach that coarsens in both $p$ and $h$, demonstrating significant efficiency gains for high-order Stokes discretizations.
Findings
$ph$MG outperforms $h$MG in setup and solve times for Taylor-Hood elements.
$ph$MG achieves low iteration counts and competitive timings for Scott-Vogelius discretizations.
Setup costs are higher for monolithic $ph$MG compared to FBF in Scott-Vogelius cases.
Abstract
This work introduces and assesses the efficiency of a monolithic MG multigrid framework designed for high-order discretizations of stationary Stokes systems using Taylor-Hood and Scott-Vogelius elements. The proposed approach integrates coarsening in both approximation order () and mesh resolution (), to address the computational and memory efficiency challenges that are often encountered in conventional high-order numerical simulations. Our numerical results reveal that MG offers significant improvements over traditional spatial-coarsening-only multigrid (MG) techniques for problems discretized with Taylor-Hood elements across a variety of problem sizes and discretization orders. In particular, the MG method exhibits superior performance in reducing setup and solve times, particularly when dealing with higher discretization orders and unstructured problem domains.…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAdvanced Numerical Methods in Computational Mathematics · Matrix Theory and Algorithms · Numerical methods for differential equations
