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A parallel time integration method for nonlinear partial differential equations is proposed. It is based on a new implementation of the Paraexp method for linear partial differential equations (PDEs) employing a block Krylov subspace…

Numerical Analysis · Mathematics 2015-09-16 G. L. Kooij , M. A. Botchev , B. J. Geurts

We present an algorithm for the solution of a simultaneous space-time discretization of linear parabolic evolution equations with a symmetric differential operator in space. Building on earlier work, we recast this discretization into a…

Numerical Analysis · Mathematics 2021-09-07 Raymond van Venetië , Jan Westerdiep

Spectral methods are renowned for their high accuracy and efficiency in solving partial differential equations. The Fourier pseudo-spectral method is limited to periodic domains and suffers from Gibbs oscillations in non-periodic problems.…

Numerical Analysis · Mathematics 2025-12-09 Dongan Li , Mou Lin , Shunxiang Cao , Shengli Chen

The problem of optimal precision switching for the conjugate gradient (CG) method applied to sparse linear systems is considered. A sparse matrix is defined as an $n\!\times\!n$ matrix with $m\!=\!O(n)$ nonzero entries. The algorithm first…

Numerical Analysis · Mathematics 2026-03-03 Alexander V. Prolubnikov

In this paper, we propose a novel, computationally efficient reduced order method to solve linear parabolic inverse source problems. Our approach provides accurate numerical solutions without relying on specific training data. The forward…

Numerical Analysis · Mathematics 2023-06-12 Yuxuan Huang , Yangwen Zhang

The stepwise coupled-mode model is a classic approach for solving range-dependent sound propagation problems. Existing coupled-mode programs have disadvantages such as high computational cost, weak adaptability to complex ocean environments…

Computational Physics · Physics 2023-03-14 Houwang Tu , Yongxian Wang , Chunmei Yang , Wei Liu , Wenbin Xiao , Xiaodong Wang

We extensively study the numerical accuracy of the well-known time splitting Fourier spectral method for the approximation of singular solutions of the Gross-Pitaevskii equation. In particular, we explore its capability of preserving a…

Numerical Analysis · Mathematics 2016-03-17 Marco Caliari , Simone Zuccher

The applicability of the Parareal parallel-in-time integration scheme for the solution of a linear, two-dimensional hyperbolic acoustic-advection system, which is often used as a test case for integration schemes for numerical weather…

Computational Engineering, Finance, and Science · Computer Science 2015-10-09 Daniel Ruprecht , Rolf Krause

The purpose of this study is to utilize the Chebyshev spectral method neural network(CSNN) model to solve differential equations. This approach employs a single-layer neural network wherein Chebyshev spectral methods are used to construct…

Numerical Analysis · Mathematics 2024-07-08 Pengsong Yin , Shuo Ling , Wenjun Ying

Time-parallel time integration has received a lot of attention in the high performance computing community over the past two decades. Indeed, it has been shown that parallel-in-time techniques have the potential to remedy one of the main…

Numerical Analysis · Mathematics 2023-09-26 Martin J. Gander , Davide Palitta

In this article, we present the time-space Chebyshev pseudospectral method (TS-CPsM) to approximate a solution to the generalised Burgers-Fisher (gBF) equation. The Chebyshev-Gauss-Lobatto (CGL) points serve as the foundation for the…

Numerical Analysis · Mathematics 2023-06-19 Harvindra Singh , Lokendra Balyan , A. K. Mittal , Parul Saini

Graph propagation (GP) computation plays a crucial role in graph data analysis, supporting various applications such as graph node similarity queries, graph node ranking, graph clustering, and graph neural networks. Existing methods, mainly…

Machine Learning · Computer Science 2024-12-17 Yichun Yang , Rong-Hua Li , Meihao Liao , Longlong Lin , Guoren Wang

Time-spectral solution of ordinary and partial differential equations is often regarded as an inefficient approach. The associated extension of the time domain, as compared to finite difference methods, is believed to result in…

Computational Physics · Physics 2017-04-14 Jan Scheffel , Kristoffer Lindvall

Spectral partitioning is a simple, nearly-linear time, algorithm to find sparse cuts, and the Cheeger inequalities provide a worst-case guarantee for the quality of the approximation found by the algorithm. Local graph partitioning…

Data Structures and Algorithms · Computer Science 2012-11-07 Shayan Oveis Gharan , Luca Trevisan

We present C programming language versions of earlier published Fortran programs (Muruganandam and Adhikari, Comput. Phys. Commun. 180 (2009) 1888) for calculating both stationary and non-stationary solutions of the time-dependent…

The present article investigates the convergence of a class of space-time discretization schemes for the Cauchy problem for linear parabolic stochastic partial differential equations (SPDEs) defined on the whole space. Sufficient conditions…

Probability · Mathematics 2012-10-04 Eric Joseph Hall

We present two accelerated numerical algorithms for single-component and binary Gross-Pitaevskii (GP) equations coupled with microwaves (electromagnetic fields) in steady state. One is based on a normalized gradient flow formulation, called…

Numerical Analysis · Mathematics 2022-02-01 Di Wang , Qi Wang

This paper develops a high-accuracy algorithm for time fractional wave problems, which employs a spectral method in the temporal discretization and a finite element method in the spatial discretization. Moreover, stability and convergence…

Numerical Analysis · Mathematics 2017-08-10 Binjie Li , Hao Luo , Xiaoping Xie

We consider an implicit finite difference scheme on uniform grids in time and space for the Cauchy problem for a second order parabolic stochastic partial differential equation where the parabolicity condition is allowed to degenerate. Such…

Numerical Analysis · Mathematics 2016-08-29 Eric Joseph Hall

We study the calibration of Gaussian process (GP) predictive distributions in the interpolation setting from a design-marginal perspective. Conditioning on the data and averaging over a design measure \mu, we formalize \mu-coverage for…

Machine Learning · Statistics 2025-12-08 Aurélien Pion , Emmanuel Vazquez