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Approximating differential operators defined on two-dimensional surfaces is an important problem that arises in many areas of science and engineering. Over the past ten years, localized meshfree methods based on generalized moving least…

Numerical Analysis · Mathematics 2023-09-11 Andrew M. Jones , Peter A. Bosler , Paul A. Kuberry , Grady B. Wright a

This note carries three purposes involving our latest advances on the radial basis function (RBF) approach. First, we will introduce a new scheme employing the boundary knot method (BKM) to nonlinear convection-diffusion problem. It is…

Computational Engineering, Finance, and Science · Computer Science 2007-05-23 W. Chen , W. He

We propose and test the first Reduced Radial Basis Function Method (R$^2$BFM) for solving parametric partial differential equations on irregular domains. The two major ingredients are a stable Radial Basis Function (RBF) solver that has an…

Numerical Analysis · Mathematics 2014-10-09 Yanlai Chen , Sigal Gottlieb , Alfa Heryudono , Akil Narayan

In this paper, we present a spectral method based on Radial Basis Functions (RBFs) for numerically solving the fully nonlinear 1D Serre Green-Naghdi equations. The approximation uses an RBF discretization in space and finite differences in…

Fluid Dynamics · Physics 2014-07-17 Maurice S. Fabien

The Intrinsic Surface Finite Element Method (ISFEM) was recently proposed to solve Partial Differential Equations (PDEs) on surfaces. ISFEM proceeds by writing the PDE with respect to a local coordinate system anchored to the surface and…

Numerical Analysis · Mathematics 2024-10-08 Elena Bachini , Mario Putti

We present a novel hyperviscosity formulation for stabilizing RBF-FD discretizations of the advection-diffusion equation. The amount of hyperviscosity is determined quasi-analytically for commonly-used explicit, implicit, and…

Numerical Analysis · Mathematics 2018-08-01 Varun Shankar , Aaron L. Fogelson

The Lane-Emden type equations are employed in the modelling of several phenomena in the areas of mathematical physics and astrophysics . In this paper a new numerical method is applied to investigate some well-known classes of Lane-Emden…

Numerical Analysis · Mathematics 2016-05-27 Kourosh Parand , Soleiman Hashemi

Polyharmonic spline (PHS) radial basis functions (RBFs) are used together with polynomials to create local RBF-finite-difference (RBF-FD) weights on different node layouts for spatial discretization of the compressible Navier-Stokes…

Computational Physics · Physics 2015-09-10 Gregory A. Barnett , Natasha Flyer , Louis J. Wicker

Based on the radial basis function (RBF), non-singular general solution and dual reciprocity principle (DRM), this paper presents an inheretnly meshless, exponential convergence, integration-free, boundary-only collocation techniques for…

Numerical Analysis · Mathematics 2025-10-20 W. Chen

We propose two localized Radial Basis Function (RBF) methods, the Radial Basis Function Partition of Unity method (RBF-PUM) and the Radial Basis Function generated Finite Differences method (RBF-FD), for solving financial derivative pricing…

Computational Finance · Quantitative Finance 2018-08-20 Slobodan Milovanović , Victor Shcherbakov

We numerically solve two-dimensional heat diffusion problems by using a simple variant of the meshfree local radial-basis function (RBF) collocation method. The main idea is to include an additional set of sample nodes outside the problem…

Computational Physics · Physics 2017-10-02 Seung Ki Baek , Minjae Kim

We present a high-order radial basis function finite difference (RBF-FD) framework for the solution of advection-diffusion equations on time-varying domains. Our framework is based on a generalization of the recently developed Overlapped…

Numerical Analysis · Mathematics 2021-09-15 Varun Shankar , Grady B. Wright , Aaron L. Fogelson

We present adaptive finite difference ENO/WENO methods by adopting infinitely smooth radial basis functions (RBFs). This is a direct extension of the non-polynomial finite volume ENO/WENO method proposed by authors in \cite{GuoJung} to the…

Numerical Analysis · Mathematics 2017-05-23 Jingyang Guo , Jae-Hun Jung

Surface partial differential equations arise in numerous scientific and engineering applications. Their numerical solution on static and evolving surfaces remains challenging due to geometric complexity and, for evolving geometries, the…

Numerical Analysis · Mathematics 2026-03-03 Jingbo Sun , Fei Wang

The Closest Point Method for solving partial differential equations (PDEs) posed on surfaces was recently introduced by Ruuth and Merriman [J. Comput. Phys. 2008] and successfully applied to a variety of surface PDEs. In this paper we study…

Numerical Analysis · Mathematics 2013-07-30 Thomas März , Colin B. Macdonald

Recently, collocation based radial basis function (RBF) partition of unity methods (PUM) for solving partial differential equations have been formulated and investigated numerically and theoretically. When combined with stable evaluation…

Numerical Analysis · Mathematics 2017-02-24 Elisabeth Larsson , Victor Shcherbakov , Alfa Heryudono

We propose a novel framework for solving nonlinear PDEs using sparse radial basis function (RBF) networks. Sparsity-promoting regularization is employed to prevent over-parameterization and reduce redundant features. This work is motivated…

Numerical Analysis · Mathematics 2026-04-28 Zihan Shao , Konstantin Pieper , Xiaochuan Tian

A major obstacle to the application of the standard Radial Basis Function-generated Finite Difference (RBF-FD) meshless method is constituted by its inability to accurately and consistently solve boundary value problems involving Neumann…

Numerical Analysis · Mathematics 2022-07-15 Riccardo Zamolo , Davide Miotti , Enrico Nobile

The paper introduces an adaptive version of the stabilized Trace Finite Element Method (TraceFEM) designed to solve low-regularity elliptic problems on level-set surfaces using a shape-regular bulk mesh in the embedding space. Two…

Numerical Analysis · Mathematics 2024-08-21 Timo Heister , Maxim A. Olshanskii , Vladimir Yushutin

Physics-informed neural networks (PINNs) have lately received significant attention as a representative deep learning-based technique for solving partial differential equations (PDEs). Most fully connected network-based PINNs use automatic…

Machine Learning · Computer Science 2024-09-30 Zixue Xiang , Wei Peng , Wen Yao