Galerkin Scheme Using Biorthogonal Wavelets on Intervals for Elliptic Interface Problems

TL;DR

This paper introduces a wavelet Galerkin method with biorthogonal wavelets for elliptic interface problems, achieving near-optimal convergence and effectively handling discontinuities across interfaces.

Contribution

It develops a wavelet-based approach that incorporates interface-adapted basis functions, providing high accuracy and robustness without complex re-meshing.

Findings

Achieves near-optimal convergence rates in $H^1$ and $L^2$ norms.

Uses dual biorthogonal wavelet basis to establish convergence.

Maintains small, bounded condition numbers regardless of problem size.

Abstract

This paper presents a wavelet Galerkin method for solving elliptic interface problems of the form $-\nabla\cdot(a\nabla u)=f$ in $\Omega\backslash \Gamma$, where $\Gamma$ is a smooth interface within $\Omega$. Since the scalar variable coefficient $a>0$ and source term $f$ are often discontinuous across $\Gamma$, the solution $u$ typically has discontinuous gradient $\nabla u$ across $\Gamma$ and hence $u\not\in H^{1.5}(\Omega)$, posing significant challenges for traditional numerical methods. By utilizing a compactly supported biorthogonal wavelet for $H^1_0(\Omega)$, we develop a strategy that incorporates additional wavelet elements (or basis functions) along the interface to resolve the complex geometry of the interface $\Gamma$ and the resulting gradient discontinuities. For the two-dimensional (2D) elliptic interface problem, the proposed method achieves near-optimal convergence rates: $\mathcal{O}(h |\log(h)|)$ in the $H^1(\Omega)$-norm and $\mathcal(h^2 |\log(h)|^2)$ in the $L^{2}$-norm with respect to the approximation order. A key theoretical contribution is the use of the dual biorthogonal wavelet basis to establish the $H^1(\Omega)$ convergence results. This is supported by the development of weighted Bessel properties for wavelets and several inequalities in fractional Sobolev spaces. To maintain high accuracy and robustness against high-contrast coefficients, our method leverages an augmented set of wavelet elements, similar to meshfree approaches, thereby eliminating the need for the complex re-meshing required by finite element methods. Unlike existing techniques, this wavelet Riesz basis framework captures the geometry of $\Gamma$ seamlessly while ensuring that the condition numbers of the coefficient matrices remain small and uniformly bounded, independent of the problem size.