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In this paper we present a high-order kernel method for numerically solving diffusion and reaction-diffusion partial differential equations (PDEs) on smooth, closed surfaces embedded in $\mathbb{R}^d$. For two-dimensional surfaces embedded…

Numerical Analysis · Mathematics 2012-06-04 Edward J. Fuselier , Grady B. Wright

This work is concerned with spectral collocation methods for fractional PDEs in unbounded domains. The method consists of expanding the solution with proper global basis functions and imposing collocation conditions on the Gauss-Hermite…

Numerical Analysis · Mathematics 2018-01-30 Tao Tang , Huifang Yuan , Tao Zhou

We propose a methodology that combines generative latent diffusion models with physics-informed machine learning to generate solutions of parametric partial differential equations (PDEs) conditioned on partial observations, which includes,…

Machine Learning · Computer Science 2026-02-11 Davide Gallon , Philippe von Wurstemberger , Patrick Cheridito , Arnulf Jentzen

We developed a new self-adjoint, consistent, and stable coupling strategy for nonlocal diffusion models, inspired by the quasinonlocal atomistic-to-continuum method for crystalline solids. The proposed coupling model is coercive with…

Numerical Analysis · Mathematics 2017-02-07 Xingjie Helen Li , Jianfeng Lu

The forward model in diffuse optical tomography (DOT) describes how light propagates through a turbid medium. It is often approximated by a diffusion equation (DE) that is numerically discretized by the classical finite element method…

Computational Physics · Physics 2019-06-04 Wenqi Lu , Jinming Duan , Joshua Deepak Veesa , Iain B. Styles

We present the Method Of Lines (MOL), which is based on the spectral collocation method, to solve space-fractional advection-diffusion equations (SFADEs) on a finite domain with variable coefficients. We focus on the cases in which the…

Numerical Analysis · Mathematics 2019-06-05 Mohammed K. Almoaeet , Mostafa Shamsi , Hassan Khosravian-Arab , Delfim F. M. Torres

A class of evolution equations with nonlocal diffusion is considered in this work. These are integro-differential equations arising as models of propagation phenomena in continuum media with nonlocal interactions including neural tissue,…

Numerical Analysis · Mathematics 2019-12-24 Dmitry Kaliuzhnyi-Verbovetskyi , Georgi S. Medvedev

This paper presents a new method for the solution of multiscale stochastic differential equations at the diffusive time scale. In contrast to averaging-based methods, e.g., the heterogeneous multiscale method (HMM) or the equation-free…

Numerical Analysis · Mathematics 2016-09-19 A. Abdulle , G. A. Pavliotis , U. Vaes

The aim of this paper is to provide a comprehensive study of some linear nonlocal diffusion problems in metric measure spaces. These include, for example, open subsets in $\mathbb{R}^N$, graphs, manifolds, multi-structures or some fractal…

Analysis of PDEs · Mathematics 2014-12-18 Aníbal Rodríguez-Bernal , Silvia Sastre-Gómez

This paper is concerned with a strongly degenerate convection-diffusion equation in one space dimension whose convective flux involves a non-linear function of the total mass to one side of the given position. This equation can be…

Numerical Analysis · Mathematics 2010-07-12 Fernando Betancourt , Raimund Bürger , Kenneth H. Karlsen

High-dimensional Partial Differential Equations (PDEs) are a popular mathematical modelling tool, with applications ranging from finance to computational chemistry. However, standard numerical techniques for solving these PDEs are typically…

Numerical Analysis · Mathematics 2023-11-22 Weiqi Wang , Simone Brugiapaglia

Problems with localized nonhomogeneous material properties present well-known challenges for numerical simulations. In particular, such problems may feature large differences in length scales, causing difficulties with meshing and…

Numerical Analysis · Mathematics 2021-11-23 Alex Viguerie , Silvia Bertoluzza , Alessandro Veneziani , Ferdinando Auricchio

In this paper, we present a model based on a local thermodynamic equilibrium, weakly ionized plasma-mixture model used for medical and technical applications in etching processes. We consider a simplified model based on the Maxwell-Stefan…

Numerical Analysis · Mathematics 2015-01-26 Juergen Geiser

We propose an implementation of linear finite element method for nonlocal diffusion problem in 2D space. In the implementation, we reduce the integral from 4D to 2D which would simplify the computation significantly.

Numerical Analysis · Mathematics 2024-08-29 Zuoqiang Shi

Spatial reaction-diffusion models have been employed to describe many emergent phenomena in biological systems. The modelling technique most commonly adopted in the literature implements systems of partial differential equations (PDEs),…

Quantitative Methods · Quantitative Biology 2015-10-05 Christian A. Yates , Mark B. Flegg

We propose an efficient numerical strategy for simulating fluid flow through porous media with highly oscillatory characteristics. Specifically, we consider non-linear diffusion models. This scheme is based on the classical homogenization…

Numerical Analysis · Mathematics 2020-02-04 Manuela Bastidas , Carina Bringedal , Sorin Pop , Florin Radu

In this paper, a non-polynomial spectral Petrov-Galerkin method and associated collocation method for substantial fractional differential equations (FDEs) are proposed, analyzed, and tested. We extend a class of generalized Laguerre…

Numerical Analysis · Mathematics 2014-08-27 Can Huang , Qingshuo Song , Zhimin Zhang

We present a consistent theoretical approach for calculating effective nonlinear susceptibilities of metamaterials taking into account both frequency and spatial dispersion. Employing the discrete dipole model, we demonstrate that effects…

Spectral and spectral element methods using Galerkin type formulations are efficient for solving linear fractional PDEs (FPDEs) of constant order but are not efficient in solving nonlinear FPDEs and cannot handle FPDEs with variable-order.…

Numerical Analysis · Mathematics 2019-03-27 Tinggang Zhao , Zhiping Mao , George Em Karniadakis

Autoregressive next-step prediction models have become the de-facto standard for building data-driven neural solvers to forecast time-dependent partial differential equations (PDEs). Denoise training that is closely related to diffusion…

Machine Learning · Computer Science 2025-03-31 Zijie Li , Anthony Zhou , Amir Barati Farimani