High dimensional finite elements for multiscale Maxwell wave equations

TL;DR

This paper introduces an efficient sparse tensor product finite element method for solving high-dimensional multiscale Maxwell wave equations, significantly reducing computational costs while maintaining accuracy.

Contribution

The authors develop an essentially optimal sparse tensor product FE method for multiscale Maxwell equations, overcoming the high computational cost of full tensor product approaches.

Findings

The method achieves near-optimal degrees of freedom for multiscale problems.

Numerical results confirm the convergence rates predicted by the analysis.

The approach effectively captures both macroscopic and microscopic information in multiscale Maxwell equations.

Abstract

We develop an essentially optimal numerical method for solving multiscale Maxwell wave equations in a domain $D\subset{\mathbb R}^d$. The problems depend on $n+1$ scales: one macroscopic scale and $n$ microscopic scales. Solving the macroscopic multiscale homogenized problem, we obtain the desired macroscopic and microscopic information. This problem depends on $n+1$ variables in ${\mathbb R}^d$, one for each scale that the original multiscale equation depends on, and is thus posed in a high dimensional tensorized domain. The straightforward full tensor product finite element (FE) method is exceedingly expensive. We develop the sparse tensor product FEs that solve this multiscale homogenized problem with essentially optimal number of degrees of freedom, that is essentially equal to that required for solving a macroscopic problem in a domain in ${\mathbb R}^d$ only, for obtaining a required level of accuracy. Numerical correctors are constructed from the FE solution. For two scale problems, we derive a rate of convergence for the numerical corrector in terms of the microscopic scale and the FE mesh width. Numerical examples confirm our analysis.