Hierarchical Finite-Element Analysis of Multiscale Electromagnetic Problems via Sparse Operator-Adapted Wavelet Decomposition

TL;DR

This paper introduces a hierarchical finite element method using operator-adapted wavelet decomposition for efficient, multiscale electromagnetic analysis, achieving high accuracy with nearly linear computational complexity.

Contribution

The method decouples resolution levels in adaptive FEM, enabling independent computations at each scale and reducing computational overhead compared to traditional coupled approaches.

Findings

Achieves high precision in multiscale electromagnetic problems.

Maintains nearly linear computational complexity.

Uses Krylov subspace solvers with ILU preconditioners for efficiency.

Abstract

In this paper, we present a finite element method (FEM) framework enhanced by an operator-adapted wavelet decomposition algorithm designed for the efficient analysis of multiscale electromagnetic problems. Usual adaptive FEM approaches, while capable of achieving the desired accuracy without requiring a complete re-meshing of the computational domain, inherently couple different resolution levels. This coupling requires recomputation of coarser-level solutions whenever finer details are added to improve accuracy, resulting in substantial computational overhead. Our proposed method addresses this issue by decoupling resolution levels. This feature enables independent computations at each scale that can be incorporated into the solutions to improve accuracy whenever needed, without requiring re-computation of coarser-level solutions. The main algorithm is hierarchical, constructing solutions from finest to coarser levels through a series of sparse matrix-vector multiplications. Due to its sparse nature, the overall computational complexity of the algorithm is nearly linear. Moreover, Krylov subspace iterative solvers are employed to solve the final linear equations, with ILU preconditioners that enhance solver convergence and maintain overall computational efficiency. The numerical experiments presented in this article verify the high precision and nearly linear computational complexity of the proposed algorithm.