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In this work, we develop a localized numerical scheme with low regularity requirements for solving time-fractional integro-differential equations. First, a fully discrete numerical scheme is constructed. Specifically, for temporal…

Numerical Analysis · Mathematics 2025-12-02 Lijing Zhao , Rui Zhao , Wenyi Tian , Yufeng Nie

Surrogate neural network-based partial differential equation (PDE) solvers have the potential to solve PDEs in an accelerated manner, but they are largely limited to systems featuring fixed domain sizes, geometric layouts, and boundary…

Machine Learning · Computer Science 2025-01-10 Chenkai Mao , Robert Lupoiu , Tianxiang Dai , Mingkun Chen , Jonathan A. Fan

We introduce the concept of data-driven finite element methods. These are finite-element discretizations of partial differential equations (PDEs) that resolve quantities of interest with striking accuracy, regardless of the underlying mesh…

Numerical Analysis · Mathematics 2022-11-15 Ignacio Brevis , Ignacio Muga , Kristoffer G. van der Zee

The Fractional Diffusion Equation (FDE) is a mathematical model that describes anomalous transport phenomena characterized by non-local and long-range dependencies which deviate from the traditional behavior of diffusion. Solving this…

Numerical Analysis · Mathematics 2023-11-14 Mohammad Partohaghighi , Emmanuel Asante-Asamani , Olaniyi S. Iyiola

We present and analyze a linearized finite element method (FEM) for the dynamical incompressible magnetohydrodynamics (MHD) equations. The finite element approximation is based on mixed conforming elements, where Taylor--Hood type elements…

Numerical Analysis · Mathematics 2019-02-20 Huadong Gao , Weifeng Qiu

This is a survey on the theory of adaptive finite element methods (AFEMs), which are fundamental in modern computational science and engineering but whose mathematical assessment is a formidable challenge. We present a self-contained and…

Numerical Analysis · Mathematics 2024-11-20 Andrea Bonito , Claudio Canuto , Ricardo H. Nochetto , Andreas Veeser

This article presents a new high-order accurate algorithm for finding a particular solution to a linear, constant-coefficient partial differential equation (PDE) by means of a convolution of the volumetric source function with the Green's…

Numerical Analysis · Mathematics 2022-10-20 Thomas G. Anderson , Hai Zhu , Shravan Veerapaneni

This paper presents a Direct Mapped Method (DMM) for real-time simulation of high switching frequency resonant converters. The DMM links state variables to diode statuses and provides an exact and noniterative solution to network equations.…

Systems and Control · Electrical Eng. & Systems 2020-06-09 Hossein Chalangar , Tarek Ould-Bachir , Keyhan Sheshyekani , Jean Mahseredjian

Elliptic partial differential equations (PDEs) with discontinuous diffusion coefficients occur in application domains such as diffusions through porous media, electro-magnetic field propagation on heterogeneous media, and diffusion…

Numerical Analysis · Mathematics 2015-01-20 Andrea Bonito , Ronald A. DeVore , Ricardo H. Nochetto

We present direct logarithmically optimal in theory and fast in practice algorithms to implement the tensor product high order finite element method on multi-dimensional rectangular parallelepipeds for solving PDEs of the Poisson kind. They…

Numerical Analysis · Mathematics 2026-01-05 Alexander Zlotnik , Ilya Zlotnik

This paper recalls the principles of the finite-element methods (FEM) theory and declines its application in the EN-MME group, for the numerical modelling and study of particle accelerator equipment. Implicit and explicit methods are…

It is well known that closed-form analytical solutions for AC power flow equations do not exist in general. This paper proposes a multi-dimensional holomorphic embedding method (MDHEM) to obtain an explicit approximate analytical AC…

Systems and Control · Computer Science 2019-03-26 Chengxi Liu , Bin Wang , Xin Xu , Kai Sun , Claus Leth Bak

This research explored a novel explicit total Lagrangian Fragile Points Method (FPM) for finite deformation of hyperelastic materials. In contrast to mesh-based methods, where mesh distortion may pose numerical challenges, meshless methods…

Elliptic interface problems whose solutions are $C^0$ continuous have been well studied over the past two decades. The well-known numerical methods include the strongly stable generalized finite element method (SGFEM) and immersed FEM…

Numerical Analysis · Mathematics 2023-02-28 Champike Attanayake , So-Hsiang Chou , Quanling Deng

Dynamic mode decomposition (DMD) is a recently developed tool for the analysis of the behavior of complex dynamical systems. In this paper, we will propose an extension of DMD that exploits low-rank tensor decompositions of potentially…

Numerical Analysis · Mathematics 2019-08-14 Stefan Klus , Patrick Gelß , Sebastian Peitz , Christof Schütte

Solving large-scale Helmholtz problems discretized with high-order finite elements is notoriously difficult, especially in 3D where direct factorization of the system matrix is very expensive and memory demanding, and robust convergence of…

Numerical Analysis · Mathematics 2025-06-23 Boris Martin , Pierre Jolivet , Christophe Geuzaine

Meshless methods are often used in numerical simulations of systems of partial differential equations (PDEs), particularly those which involve complex geometries or free surfaces. Here we present a novel compact scheme based on the local…

Numerical Analysis · Mathematics 2026-03-13 Henry M. Broadley , Steven J. Lind , Jack R. C. King

Boundary element methods (BEM) reduce a partial differential equation in a domain to an integral equation on the domain's boundary. They are particularly attractive for solving problems on unbounded domains, but handling the dense matrices…

Numerical Analysis · Mathematics 2020-06-30 Steffen Börm

In this paper we develop and analyse domain decomposition methods for linear systems of equations arising from conforming finite element discretisations of positive Maxwell-type equations. Convergence of domain decomposition methods rely…

Numerical Analysis · Mathematics 2021-07-08 Niall Bootland , Victorita Dolean , Frédéric Nataf , Pierre-Henri Tournier

Neural Stochastic Differential Equations (Neural SDEs) have emerged as powerful mesh-free generative models for continuous stochastic processes, with critical applications in fields such as finance, physics, and biology. Previous…

Machine Learning · Computer Science 2025-03-28 Jianxin Zhang , Josh Viktorov , Doosan Jung , Emily Pitler