Searcharxiv⌕ Search

arXiv · 2610.09765

Operator preconditioning of the Costabel FEM-BEM coupling for the time-harmonic Maxwell transmission problem

Abstract

This paper derives an equivalent formulation of the Costabel FEM-BEM coupling for the time-harmonic Maxwell transmission problem with a Lipschitz interface, and constructs a block-diagonal preconditioner for the resulting discrete system. The reformulated coupling is posed on a product of trace spaces and is obtained by introducing a trace lifting operator and the Steklov--Poincar{\' e} operator. At the algebraic level, it corresponds to eliminating the interior FEM unknowns, resulting in a discrete system that involves the Schur complement of the interior FEM block. Assuming that the frequency lies outside the resonance sets, we prove that the reformulated coupling is equivalent to the Costabel coupling at both the continuous and discrete levels. Using the operator preconditioning framework, we build a preconditioner from the Galerkin matrix induced by the Maxwell single layer boundary integral operator. The spectral condition number of the preconditioned matrix is shown to be uniformly bounded. Numerical experiments illustrate the practical effectiveness of the proposed approach.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Quang Huy Nguyen, Van Chien Le, Kristof Cools. 2026-10-07. Operator preconditioning of the Costabel FEM-BEM coupling for the time-harmonic Maxwell transmission problem. https://arxiv.org/abs/2610.09765

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Asymptotic error distributions for diffusion-implicit schemes via appurtenant methods

We develop an appurtenant-method framework for analyzing asymptotic error distributions and implement it in two diffusion-implicit settings for stochastic differential equations. The argument rests on an explicit appurtenant method which has the same strong order as the implicit method and approximates it with a higher strong order. A local-to-global strong convergence result for two Markov chains turns the one-step expansion of the implicit equation into the required appurtenant method; the limiting distribution can then be obtained from an adapted continuization of the appurtenant method. We first illustrate the framework with the fully implicit stochastic midpoint method for a multidimensional Stratonovich equation, without imposing commutativity on the diffusion fields. This method has strong order $1/2$, and its normalized error is governed by the Lévy areas omitted by the numerical method. We then turn to the main application, namely, a class of fully implicit stochastic Runge--Kutta (SRK) methods of strong order $1$ for Stratonovich equations with scalar multiplicative noise. We derive their asymptotic error distributions and identify a parameter, depending only on the coefficients of the Butcher tableau, that reflects the growth rate of the root-mean-square error of the SRK method.

math.NA↗

On the Pre-Asymptotic Stability and Inverse Structure of Extended-Domain Spectral Methods

The extended-domain method is a strategy for applying spectral methods to complex geometries. Its stability is complicated by the ill-conditioning of the Fourier extension frame. This paper provides an analysis of the method's pre-asymptotic behavior. We confirm that the spectral collocation system is asymptotically ill-conditioned for both the Poisson and convection-diffusion operators, driven by the redundancy of the underlying frame. However, we prove a fundamental structural dichotomy in their discrete Green's functions. We show that the inverse of the convection-diffusion operator is numerically highly asymmetric, exhibiting exponential upstream decay, in contrast to the numerically dense inverse of the Poisson operator. This intrinsic asymmetry explains why the convection-diffusion operator is significantly more robust to the underlying frame instability in practical computations.

math.NA↗

Neural Approximate Inverse Preconditioners

In this paper, we propose a learning-based framework for constructing approximate inverse preconditioners for partial differential equations (PDEs) by approximating the Green's function of the underlying differential operator with neural networks (NNs). The training framework combines four components: an adaptive multiscale neural architecture ($α$MSNN) that resolves hierarchical features across near-, middle-, and far-field regimes; optional coarse-grid solution anchors that improve identifiability; a multistage training strategy that progressively refines the Green's function representation across spatial scales; and an overlapping domain decomposition that enables local adaptation while preserving global consistency. Once trained, the learned Green's function provides a continuous approximation to the inverse operator that can be discretized on a given mesh to construct approximate inverse preconditioners. Within the domain decomposition framework, selected blocks of the learned inverse can also be used to approximate the inverse Schur complement, avoiding the need to assemble a full dense inverse. Numerical experiments on challenging PDE problems demonstrate that the resulting preconditioners provide robust and scalable convergence and are competitive with classical two-level preconditioning methods.

math.NA↗