A Parallel and Adaptive Mesh-Free method for Heterogeneous Porous Media
For computational geoscience and porous media simulation, this provides a mesh-independent way to handle discontinuous material properties, enabling consistent evaluation across different discretizations.
The paper introduces PAM, a mesh-free framework using radial basis functions with Shepard-normalization and sparse regression to approximate discontinuous, piecewise constant data in heterogeneous porous media. The method achieves accurate, adaptive, and scalable approximations that preserve sharp interfaces, with theoretical guarantees on L1 error and demonstrated performance on challenging permeability fields.
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.