Discrete ABP Estimate and Convergence Rates for Linear Elliptic Equations in Non-divergence Form

TL;DR

This paper develops a finite element method for linear elliptic equations in non-divergence form, establishing discrete ABP estimates and convergence rates in the max norm, applicable to non-convex domains.

Contribution

It introduces a two-scale FEM with a discrete ABP estimate, providing quasi-optimal convergence rates for non-divergence form elliptic PDEs.

Findings

FEM satisfies the discrete maximum principle under certain mesh conditions.

Discrete ABP estimate is established for finite element analysis.

Achieves pointwise error estimates with near-optimal convergence rates.

Abstract

We design a two-scale finite element method (FEM) for linear elliptic PDEs in non-divergence form $A(x) : D^2 u(x) = f(x)$ in a bounded but not necessarily convex domain $\Omega$ and study it in the max norm. The fine scale is given by the meshsize $h$ whereas the coarse scale $\epsilon$ is dictated by an integro-differential approximation of the PDE. We show that the FEM satisfies the discrete maximum principle (DMP) for any uniformly positive definite matrix $A$ provided that the mesh is face weakly acute. We establish a discrete Alexandroff-Bakelman-Pucci (ABP) estimate which is suitable for finite element analysis. Its proof relies on a discrete Alexandroff estimate which expresses the min of a convex piecewise linear function in terms of the measure of its sub-differential, and thus of jumps of its gradient. The discrete ABP estimate leads, under suitable regularity assumptions on $A$ and $u$, to pointwise error estimates of the form \begin{equation*} \| u - u^{\epsilon}_h \|_{L_\infty(\Omega)} \leq \, C(A,u) \, h^{2\alpha /(2 + \alpha)} \big| \ln h \big| \qquad 0< \alpha \leq 2, \end{equation*} provided $\epsilon \approx h^{2/(2+\alpha)}$. Such a convergence rate is at best of order $ h \big| \ln h \big|$, which turns out to be quasi-optimal.