A structure preserving H-curl algebraic multigrid method for the eddy current equations
Raymond Tuminaro, Christian Glusa

TL;DR
This paper introduces a new algebraic multigrid method for eddy current equations that preserves key mathematical properties without relying on simple interpolation, enabling more efficient and mesh-independent solutions.
Contribution
The paper presents a structure preserving H-curl algebraic multigrid method that enforces commuting relationships using energy minimization, improving upon previous methods that relied on sub-optimal interpolation.
Findings
Achieves mesh independent convergence rates.
Enables use of sophisticated nodal interpolation operators.
Demonstrates effectiveness on various test problems.
Abstract
A new algebraic multigrid method (AMG) is presented for solving the linear systems associated with the eddy current approximation to the Maxwell equations. This AMG method extends an idea proposed by Reitzinger and Schoberl. The main feature of the Reitzinger and Schoberl algorithm (RSAMG) is that it maintains null-space properties of the Curl-Curl operator throughout all levels of the AMG hierarchy. It does this by enforcing a commuting relationship involving grid transfers and the discrete gradient operator. This null-space preservation property is critical to the algorithm's success, however enforcing this commuting relationship is non-trivial except in the special case where one leverages a piece-wise constant nodal interpolation operator. For this reason, mesh independent convergence rates are generally not observed for RSAMG due to its reliance on sub-optimal piece-wise constant…
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 · Electromagnetic Simulation and Numerical Methods · Model Reduction and Neural Networks
