A Parallel and Adaptive Mesh-Free method for Heterogeneous Porous Media

TL;DR

This paper introduces PAM, a mesh-free, parallel, and adaptive approximation method using RBFs for representing discontinuous data in heterogeneous porous media, ensuring accuracy and scalability across different discretizations.

Contribution

The work develops a novel mesh-independent RBF-based framework with Shepard-normalization and adaptive refinement for discontinuous data approximation in porous media.

Findings

Achieves arbitrarily small L1 error in approximating step functions.

Demonstrates high accuracy and scalability in heterogeneous permeability fields.

Employs parallel domain decomposition for computational efficiency.

Abstract

Material properties such as permeability fields in heterogeneous porous media are often represented as discontinuous, piecewise constant data tied to a given spatial discretization. Such representations are inherently mesh-dependent, requiring interpolation or projection whenever they are transferred to a different discretization. In this work, we develop \emph{Parallel and Adaptive Mesh-Free Approximation (PAM)}, a mesh-independent framework that approximates discontinuous data by a continuous, closed-form function. The resulting approximation can be evaluated consistently across different geometries and numerical discretizations, while preserving sharp interface features. The proposed PAM framework employs radial basis functions (RBFs) to construct continuous approximations of discontinuous data. To accurately capture discontinuities, we incorporate Shepard-normalization, which stabilizes the approximation near sharp interfaces. The coefficients of the RBF expansion are determined via sparse regression, enabling automatic selection of the most relevant basis functions and promoting robust representations. In addition, we develop a novel adaptive refinement approach which further enriches the approximation in regions of rapid spatial variation. We provide a theoretical analysis showing that the proposed normalized RBF framework achieves arbitrarily small $L^1$ error in approximating discontinuous step functions. To enhance computational efficiency, the domain is partitioned into subdomains, and the reconstruction problem is solved independently on each subdomain in parallel. Numerical experiments demonstrate the accuracy, adaptivity, and scalability of the proposed method, including applications to challenging heterogeneous permeability fields.