Ghost Point Diffusion Maps for solving elliptic PDE's on Manifolds with Classical Boundary Conditions

TL;DR

This paper introduces Ghost Point Diffusion Maps (GPDM), a novel kernel method extension that accurately solves elliptic PDEs on manifolds with boundaries by incorporating ghost points, improving boundary convergence and accuracy.

Contribution

The paper develops GPDM, extending diffusion maps with ghost points to handle boundary conditions in elliptic PDEs on manifolds, ensuring convergence and improved accuracy.

Findings

GPDM converges pointwise near boundaries unlike standard DM.

GPDM outperforms DM in solving elliptic eigenvalue problems.

Numerical validation on various manifolds confirms effectiveness.

Abstract

In this paper, we extend the class of kernel methods, the so-called diffusion maps (DM), and its local kernel variants, to approximate second-order differential operators defined on smooth manifolds with boundaries that naturally arise in elliptic PDE models. To achieve this goal, we introduce the Ghost Point Diffusion Maps (GPDM) estimator on an extended manifold, identified by the set of point clouds on the unknown original manifold together with a set of ghost points, specified along the estimated tangential direction at the sampled points at the boundary. The resulting GPDM estimator restricts the standard DM matrix to a set of extrapolation equations that estimates the function values at the ghost points. This adjustment is analogous to the classical ghost point method in finite-difference scheme for solving PDEs on flat domain. As opposed to the classical DM which diverges near the boundary, the proposed GPDM estimator converges pointwise even near the boundary. Applying the consistent GPDM estimator to solve the well-posed elliptic PDEs with classical boundary conditions (Dirichlet, Neumann, and Robin), we establish the convergence of the approximate solution under appropriate smoothness assumptions. We numerically validate the proposed mesh-free PDE solver on various problems defined on simple sub-manifolds embedded in Euclidean spaces as well as on an unknown manifold. Numerically, we also found that the GPDM is more accurate compared to DM in solving elliptic eigenvalue problems on bounded smooth manifolds.