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This paper aims to survey our recent work relating to the radial basis function (RBF) and its applications to numerical PDEs. We introduced the kernel RBF involving general pre-wavelets and scale-orthogonal wavelets RBF. A…

Numerical Analysis · Mathematics 2025-10-20 W Chen

We study the numerical evaluation of the integral fractional Laplacian and its application in solving fractional diffusion equations. We derive a pseudo-spectral formula for the integral fractional Laplacian operator based on fractional…

Numerical Analysis · Mathematics 2024-11-19 Zhaopeng Hao , Zhiqiang Cai , Zhongqiang Zhang

Partial differential equations (PDEs) on surfaces are fundamental to scientific computing and geometry processing. A popular approach to solving PDEs on surfaces is the finite element method (FEM), where the surface is divided into discrete…

Graphics · Computer Science 2026-05-27 Pranav Jain , Navami Kairanda , Peter Yichen Chen , Oded Stein

This paper presents a new finite difference method, called {\varphi}-FD, inspired by the {\phi}-FEM approach for solving elliptic partial differential equations (PDEs) on general geometries. The proposed method uses Cartesian grids,…

Numerical Analysis · Mathematics 2025-05-28 Michel Duprez , Vanessa Lleras , Alexei Lozinski , Vincent Vigon , Killian Vuillemot

In this paper, we propose a mesh-free numerical method for solving elliptic PDEs on unknown manifolds, identified with randomly sampled point cloud data. The PDE solver is formulated as a spectral method where the test function space is the…

Numerical Analysis · Mathematics 2023-05-03 Qile Yan , Shixiao Jiang , John Harlim

Machine learning based partial differential equations (PDEs) solvers have received great attention in recent years. Most progress in this area has been driven by deep neural networks such as physics-informed neural networks (PINNs) and…

Numerical Analysis · Mathematics 2025-09-23 Chunyang Liao

Semi-lagrangian schemes for discretization of the dynamic programming principle are based on a time discretization projected on a state-space grid. The use of a structured grid makes this approach not feasible for high-dimensional problems…

Numerical Analysis · Mathematics 2023-06-09 Alessandro Alla , Hugo Oliveira , Gabriele Santin

We describe an efficient method for the approximation of functions using radial basis functions (RBFs), and extend this to a solver for boundary value problems on irregular domains. The method is based on RBFs with centers on a regular grid…

Numerical Analysis · Mathematics 2024-03-05 Yiqing Zhou , Daan Huybrechs

Divergence-free (div-free) and curl-free vector fields are pervasive in many areas of science and engineering, from fluid dynamics to electromagnetism. A common problem that arises in applications is that of constructing smooth approximants…

Numerical Analysis · Mathematics 2021-02-18 Kathryn P. Drake , Edward J. Fuselier , Grady B. Wright

In this paper, we study a numerical method for the solution of partial differential equations on evolving surfaces. The numerical method is built on the stabilized trace finite element method (TraceFEM) for the spatial discretization and…

Numerical Analysis · Mathematics 2018-03-23 Christoph Lehrenfeld , Maxim A. Olshanskii , Xianmin Xu

Solving inverse and optimization problems over solutions of nonlinear partial differential equations (PDEs) on complex spatial domains is a long-standing challenge. Here we introduce a method that parameterizes the solution using spectral…

Numerical Analysis · Mathematics 2025-10-30 James V. Roggeveen , Michael P. Brenner

The growing availability of computational resources has significantly increased the interest of the scientific community in performing complex multi-physics and multi-domain simulations. However, the generation of appropriate computational…

Numerical Analysis · Mathematics 2026-04-03 Daniele Moretto , Andrea Franceschini , Massimiliano Ferronato

In this paper, we propose compactly supported radial basis functions for solving some well- known classes of astrophysics problems categorized as non-linear singular initial ordinary dif- ferential equations on a semi-infinite domain. To…

Numerical Analysis · Mathematics 2016-05-31 Kourosh Parand , Mohammad Hemami

We present a new hyperviscosity formulation for stabilizing radial basis function-finite difference (RBF-FD) discretizations of advection-diffusion-reaction equations on manifolds $\mathbb{M} \subset \mathbb{R}^3$ of co-dimension one. Our…

Numerical Analysis · Mathematics 2020-04-27 Varun Shankar , Grady B. Wright , Akil Narayan

We develop efficient and high-order accurate finite difference methods for elliptic partial differential equations in complex geometry in the Difference Potentials framework. The main novelty of the developed schemes is the use of local…

Numerical Analysis · Mathematics 2023-06-28 Qing Xia

Chebyshev pseudospectral (PS) methods are reported to provide highly accurate solution using polynomial approximation. Use of polynomial basis functions in PS algorithms limits the formulation to univariate systems constraining it to tensor…

Computational Physics · Physics 2015-12-01 Pankaj K Mishra , Sankar K Nath

We mainly concerned with a decoupled fractional Laplacian wave equation in this paper. A new time-space domain radial basis function (RBF) collocation method is introduced to solve the fractional wave equation, which describes seismic wave…

Computational Physics · Physics 2018-06-07 Yiran Xu , Jingye Li , Guofei Pang , Zhikai Wang , Xiaohong Chen , Benfeng Wang

In this paper we consider a class of unfitted finite element methods for discretization of partial differential equations on surfaces. In this class of methods known as the Trace Finite Element Method (TraceFEM), restrictions or traces of…

Numerical Analysis · Mathematics 2017-07-06 Maxim A. Olshanskii , Arnold Reusken

The meshless/meshfree radial basis function (RBF) method is a powerful technique for interpolating scattered data. But, solving large RBF interpolation problems without fast summation methods is computationally expensive. For RBF…

Numerical Analysis · Mathematics 2016-06-27 Wei Zhao , Martin Stoll

In this paper, we consider approximating the parameter-to-solution maps of parametric partial differential equations (PPDEs) using deep neural networks (DNNs). We propose an efficient approach combining reduced collocation methods (RCMs)…

Numerical Analysis · Mathematics 2025-08-18 Guanhang Lei , Zhen Lei , Lei Shi , Chenyu Zeng
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