Adaptive Finite Element Methods for Elliptic Problems with Discontinuous Coefficients

TL;DR

This paper introduces adaptive finite element methods for elliptic PDEs with discontinuous coefficients, especially when discontinuities are unknown or do not align with mesh boundaries, using a new $L_q$ distortion approach.

Contribution

It proposes a novel $L_q$ distortion-based framework that relaxes the need for exact discontinuity matching in finite element methods for elliptic problems.

Findings

AFEMs are optimal in distortion versus computational effort

New $L_q$ distortion theory handles unknown discontinuities

Numerical results validate the theoretical analysis

Abstract

Elliptic partial differential equations (PDEs) with discontinuous diffusion coefficients occur in application domains such as diffusions through porous media, electro-magnetic field propagation on heterogeneous media, and diffusion processes on rough surfaces. The standard approach to numerically treating such problems using finite element methods is to assume that the discontinuities lie on the boundaries of the cells in the initial triangulation. However, this does not match applications where discontinuities occur on curves, surfaces, or manifolds, and could even be unknown beforehand. One of the obstacles to treating such discontinuity problems is that the usual perturbation theory for elliptic PDEs assumes bounds for the distortion of the coefficients in the $L_\infty$ norm and this in turn requires that the discontinuities are matched exactly when the coefficients are approximated. We present a new approach based on distortion of the coefficients in an $L_q$ norm with $q<\infty$ which therefore does not require the exact matching of the discontinuities. We then use this new distortion theory to formulate new adaptive finite element methods (AFEMs) for such discontinuity problems. We show that such AFEMs are optimal in the sense of distortion versus number of computations, and report insightful numerical results supporting our analysis.