A Spectral Low-Mode Reduced Method for Elliptic Problems

TL;DR

This paper introduces a spectral low-mode reduced solver for elliptic boundary value problems that offers an efficient, accurate, and coefficient-heterogeneity-preserving alternative to traditional numerical methods, with demonstrated speedups and robust performance.

Contribution

The paper presents a novel spectral low-mode reduction approach projecting onto eigenmodes, providing an analytic, training-free, and energy-optimal reduced model for elliptic problems.

Findings

Achieves $rac{ ext{sqrt(log M)}}{M}$ decay in truncation error for $H^2$ solutions.

Provides well-conditioned projected operators with mesh refinement.

Demonstrates speedups over direct solvers and competitive performance with multigrid methods.

Abstract

We develop a spectral low-mode reduced solver for second-order elliptic boundary value problems with spatially varying diffusion coefficients. The approach projects standard finite difference or finite element discretization onto a global coarse space spanned by the lowest Dirichlet Laplacian eigenmodes, yielding an analytic reduced model that requires no training data and preserves coefficient heterogeneity through an exact Galerkin projection. The reduced solution is energy-optimal in the selected subspace and, for $H^2$-regular solutions, the truncation error associated with discarded modes satisfies a $\sqrt{\log M}/M$ decay in the $H_0^1$ norm. For uniformly stable reduced bases, the projected operator is well conditioned with respect to mesh refinement, and numerical experiments corroborate the predicted accuracy and demonstrate meaningful speedups over sparse direct solvers, with favorable performance relative to multigrid and deflation-based Krylov methods for heterogeneous coefficients in the tested setups.