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We analyze rates of uniform convergence for a class of high-order semi-Lagrangian schemes for first-order, time-dependent partial differential equations on embedded submanifolds of $\mathbb{R}^d$ (including advection equations on surfaces)…

Numerical Analysis · Mathematics 2025-08-15 Thomas Hangelbroek , Christian Rieger , Grady B. Wright

Stability and optimal convergence analysis of a non-uniform implicit-explicit L1 finite element method (IMEX-L1-FEM) is studied for a class of time-fractional linear partial differential/integro-differential equations with non-self-adjoint…

Numerical Analysis · Mathematics 2024-03-19 Aditi Tomar , Lok Pati Tripathi , Amiya K. Pani

We present the first method to directly use a learned continuous Lagrangian to forecast the dynamics of systems governed by partial differential equations, exploiting the inherent conservative structure to achieve stable long-range…

Machine Learning · Computer Science 2026-05-11 Lyra Zhornyak , Eric Forgoston , M. Ani Hsieh

We present a unified framework for analyzing local SGD methods in the convex and strongly convex regimes for distributed/federated training of supervised machine learning models. We recover several known methods as a special case of our…

Machine Learning · Computer Science 2020-11-06 Eduard Gorbunov , Filip Hanzely , Peter Richtárik

We propose a high-precision numerical quadrature framework based on local Fourier extension (LFE) approximations. The method constructs, on each subinterval, a truncated-SVD stabilized local Fourier continuation of the integrand on an…

Numerical Analysis · Mathematics 2026-03-17 Xinran Liu , Zhenyu Zhao , Benxue Gong

This paper presents a novel multi-scale method for elliptic partial differential equations with arbitrarily rough coefficients. In the spirit of numerical homogenization, the method constructs problem-adapted ansatz spaces with uniform…

Numerical Analysis · Mathematics 2024-08-05 Philip Freese , Moritz Hauck , Tim Keil , Daniel Peterseim

The Local Fourier analysis (LFA) is a classic tool to prove convergence theorems for multigrid methods (MGMs). In particular, we are interested in optimality that is a convergence speed independent of the size of the involved matrices. For…

Numerical Analysis · Mathematics 2008-07-17 Marco Donatelli

We introduce a new concept of sparsity for the stochastic elliptic operator $-{\rm div}\left(a(x,\omega)\nabla(\cdot)\right)$, which reflects the compactness of its inverse operator in the stochastic direction and allows for spatially…

Numerical Analysis · Mathematics 2015-06-22 Thomas Y. Hou , Pengfei Liu

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

Computing the stiffness matrix for the finite element discretization of the nonlocal Laplacian on unstructured meshes is difficult, because the operator is nonlocal and can even be singular. In this paper, we focus on the $C^0$-piecewise…

Numerical Analysis · Mathematics 2025-09-30 Changtao Sheng , Huiyuan Li , Huifang Yuan , Li-Lian Wang

For linear parabolic initial-boundary value problems with self-adjoint, time-homogeneous elliptic spatial operator in divergence form with Lipschitz-continuous coefficients, and for incompatible, time-analytic forcing term in…

Numerical Analysis · Mathematics 2022-03-23 Ilaria Perugia , Christoph Schwab , Marco Zank

We analyze adaptive mesh-refining algorithms for conforming finite element discretizations of certain non-linear second-order partial differential equations. We allow continuous polynomials of arbitrary, but fixed polynomial order. The…

Numerical Analysis · Mathematics 2014-03-14 Michael Feischl , Thomas Führer , Dirk Praetorius

In this work, we consider alternative discretizations for PDEs which use expansions involving integral operators to approximate spatial derivatives. These constructions use explicit information within the integral terms, but treat boundary…

Computational Physics · Physics 2024-11-12 Andrew J. Christlieb , Pierson T. Guthrey , William A. Sands , Mathialakan Thavappiragasm

A discrete analysis of the phase and dissipation errors of an explicit, semi-Lagrangian spectral element method is performed. The semi-Lagrangian method advects the Lagrange interpolant according the Lagrangian form of the transport…

Numerical Analysis · Mathematics 2023-02-08 Gustaaf B. Jacobs , Hareshram Natarajan , Pavel Popov , David A. Kopriva

Equilibrium Propagation (EP) is a learning algorithm for training Energy-based Models (EBMs) on static inputs which leverages the variational description of their fixed points. Extending EP to time-varying inputs is a challenging problem,…

Machine Learning · Computer Science 2026-04-14 Guillaume Pourcel , Debabrota Basu , Maxence Ernoult , Aditya Gilra

Dynamic mode decomposition (DMD) is a widely used data-driven algorithm for predicting the future states of dynamical systems. However, its standard formulation often struggles with poor long-term predictive accuracy. To address this…

Numerical Analysis · Mathematics 2026-04-21 Qiuqi Li , Chang Liu , Yifei Yang

This paper investigates the equations of motion for a relativistic charged particle in a general magnetic field. By reformulating the dynamics in four-dimensional spacetime and separating the linear and nonlinear parts, we construct an…

Numerical Analysis · Mathematics 2026-03-24 Zhirui Shen , Bin Wang

We propose a Lawson-time-splitting extended Fourier pseudospectral (LTSeFP) method for the numerical integration of the Gross-Pitaevskii equation with time-dependent potential that is of low regularity in space. For the spatial…

Numerical Analysis · Mathematics 2025-04-29 Bo Lin , Ying Ma , Chushan Wang

Recent years have seen the emergence of nonlinear methods for solving partial differential equations (PDEs), such as physics-informed neural networks (PINNs). While these approaches often perform well in practice, their theoretical analysis…

Numerical Analysis · Mathematics 2025-08-27 Alexandre Magueresse , Santiago Badia

In this article we develop convergence theory for a general class of adaptive approximation algorithms for abstract nonlinear operator equations on Banach spaces, and use the theory to obtain convergence results for practical adaptive…

Numerical Analysis · Mathematics 2010-01-12 Michael Holst , Gantumur Tsogtgerel , Yunrong Zhu
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