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In this paper, we propose a novel meshfree Generalized Finite Difference Method (GFDM) approach to discretize PDEs defined on manifolds. Derivative approximations for the same are done directly on the tangent space, in a manner that mimics…

Numerical Analysis · Mathematics 2019-05-14 Pratik Suchde , Joerg Kuhnert

In this paper, we extend the Generalized Moving Least-Squares (GMLS) method in two different ways to solve the vector-valued PDEs on unknown smooth 2D manifolds without boundaries embedded in $\mathbb{R}^{3}$, identified with randomly…

Numerical Analysis · Mathematics 2025-10-24 Rongji Li , Qile Yan , Shixiao W. Jiang

Solving partial differential equations (PDEs) on manifolds defined by randomly sampled point clouds is a challenging problem in scientific computing and has broad applications in various fields. In this paper, we develop a two-step…

Numerical Analysis · Mathematics 2025-12-17 Rongji Li , Haichuan Di , Shixiao Willing Jiang

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…

Numerical Analysis · Mathematics 2021-03-18 Shixiao W. Jiang , John Harlim

In recent work, Li et al.\ (Comm.\ Math.\ Sci., 7:81-107, 2009) developed a diffuse-domain method (DDM) for solving partial differential equations in complex, dynamic geometries with Dirichlet, Neumann, and Robin boundary conditions. The…

Numerical Analysis · Mathematics 2015-05-18 Karl Yngve Lervåg , John Lowengrub

This paper makes the first attempt to apply newly developed upwind GFDM for the meshless solution of two-phase porous flow equations. In the presented method, node cloud is used to flexibly discretize the computational domain, instead of…

Numerical Analysis · Mathematics 2022-04-19 Xiang Rao , Yina Liu , Hui Zhao

In this paper, we evaluate the effectiveness of deep operator networks (DeepONets) in solving both forward and inverse problems of partial differential equations (PDEs) on unknown manifolds. By unknown manifolds, we identify the manifold by…

Numerical Analysis · Mathematics 2024-07-09 Anran Jiao , Qile Yan , Jhn Harlim , Lu Lu

In this paper, we extend the class of kernel methods, the so-called diffusion maps (DM) and ghost point diffusion maps (GPDM), to solve the time-dependent advection-diffusion PDE on unknown smooth manifolds without and with boundaries. The…

Numerical Analysis · Mathematics 2021-05-31 Qile Yan , Shixiao Willing Jiang , John Harlim

Given only a collection of points sampled from a Riemannian manifold embedded in a Euclidean space, in this paper we propose a new method to solve elliptic partial differential equations (PDEs) supplemented with boundary conditions. Notice…

Numerical Analysis · Mathematics 2022-11-29 Ryan Vaughn , Tyrus Berry , Harbir Antil

The diffuse-domain, or smoothed boundary, method is an attractive approach for solving partial differential equations in complex geometries because of its simplicity and flexibility. In this method the complex geometry is embedded into a…

Numerical Analysis · Mathematics 2019-12-02 Fei Yu , Zhenlin Guo , John Lowengrub

In this paper, a meshless Generalized Finite Difference Method (GFDM) is proposed to deal with the Stokes-Darcy coupled problem with the Beavers-Joseph-Saffman (BJS) interface conditions. Some high order GFDMs are proposed to show the…

Numerical Analysis · Mathematics 2025-06-24 Yanan Xing , Haibiao Zheng

This article presents a novel approach to construct Intrinsic Gaussian Processes for regression on unknown manifolds with probabilistic metrics (GPUM) in point clouds. In many real world applications, one often encounters high dimensional…

Machine Learning · Statistics 2023-01-18 Mu Niu , Zhenwen Dai , Pokman Cheung , Yizhu Wang

We present a novel Eulerian meshless method for two-phase flows with arbitrary embedded geometries. The spatial derivatives are computed using the meshless generalized finite difference method (GFDM). The sharp phase interface is tracked…

Fluid Dynamics · Physics 2024-06-27 Anand S Bharadwaj , Pratik Suchde , Prapanch Nair

In this work, a Generalized Finite Difference (GFD) scheme is presented for effectively computing the numerical solution of a parabolic-elliptic system modelling a bacterial strain with density-suppressed motility. The GFD method is a…

Numerical Analysis · Mathematics 2024-01-29 Federico Herrero-Hervás

In this paper, an upwind GFDM is developed for the coupled heat and mass transfer problems in porous media. GFDM is a meshless method that can obtain the difference schemes of spatial derivatives by using Taylor expansion in local node…

Numerical Analysis · Mathematics 2022-05-02 Xiang Rao

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

In this paper, the generalized finite element method (GFEM) for solving second order elliptic equations with rough coefficients is studied. New optimal local approximation spaces for GFEMs based on local eigenvalue problems involving a…

Numerical Analysis · Mathematics 2021-12-22 Chupeng Ma , Robert Scheichl , Tim Dodwell

Derivative boundary conditions introduce challenges for mesh-free discretizations of PDEs on surfaces, especially when the domain is represented by randomly sampled point clouds. The recently developed two-step tangent-space RBF-generated…

Numerical Analysis · Mathematics 2026-03-31 Peng Chen , Shixiao Willing Jiang , Rongji Li , Qile Yan

This paper applies the gradient discretisation method (GDM) for fourth order elliptic variational inequalities. The GDM provides a new formulation of error estimates and a complete convergence analysis of several numerical methods. We show…

Numerical Analysis · Mathematics 2023-05-31 Yahya Alnashri

In much of the literature on function approximation by deep networks, the function is assumed to be defined on some known domain, such as a cube or a sphere. In practice, the data might not be dense on these domains, and therefore, the…

Machine Learning · Computer Science 2020-08-21 Hrushikesh Mhaskar
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