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A new method for solving numerically stochastic partial differential equations (SPDEs) with multiple scales is presented. The method combines a spectral method with the heterogeneous multiscale method (HMM) presented in [W. E, D. Liu, and…

Numerical Analysis · Mathematics 2015-05-28 A. Abdulle , G. A. Pavliotis

This paper presents an energy-preserving machine learning method for inferring reduced-order models (ROMs) by exploiting the multi-symplectic form of partial differential equations (PDEs). The vast majority of energy-preserving…

Machine Learning · Computer Science 2024-09-17 Süleyman Yıldız , Pawan Goyal , Peter Benner

We design a consistent Galerkin scheme for the approximation of the vectorial modified Korteweg-de Vries equation. We demonstrate that the scheme conserves energy up to machine precision. In this sense the method is consistent with the…

Numerical Analysis · Mathematics 2017-10-11 James Jackaman , Georgios Papamikos , Tristan Pryer

The main objective of this paper is to present an efficient structure-preserving scheme, which is based on the idea of the scalar auxiliary variable approach, for solving the space fractional nonlinear Schr\"{o}dinger equation. First, we…

Numerical Analysis · Mathematics 2019-11-19 Yayun Fu , Wenjun Cai , Yushun Wang

In this paper we discuss three symbolic approaches for the generation of a finite difference scheme of a partial differential equation (PDE). We prove, that for a linear PDE with constant coefficients these three approaches are equivalent…

Mathematical Physics · Physics 2019-03-06 Viktor Levandovskyy , Bernd Martin

Structure-preserving algorithms for solving conservative PDEs with added linear dissipation are generalized to systems with time-dependent damping/driving terms. This study is motivated by several PDE models of physical phenomena, such as…

Numerical Analysis · Mathematics 2018-04-09 Ashish Bhatt , Brian E. Moore

For the parallel computation of partial differential equations, one key is the grid partitioning. It requires that each process owns the same amount of computations, and also, the partitioning quality should be proper to reduce the…

Distributed, Parallel, and Cluster Computing · Computer Science 2017-02-02 Hui Liu , Tao Cui , Wei Leng , Linbo Zhang

In this article, we propose a two-grid based adaptive proper orthogonal decomposition (POD) method to solve the time dependent partial differential equations. Based on the error obtained in the coarse grid, we propose an error indicator for…

Numerical Analysis · Mathematics 2020-07-24 Xiaoying Dai , Xiong Kuang , Jack Xin , Aihui Zhou

We present a strategy for solving time-dependent problems on grids with local refinements in time using different time steps in different regions of space. We discuss and analyze two conservative approximations based on finite volume with…

Numerical Analysis · Mathematics 2008-12-18 Isabelle Faille , Frédéric Nataf , Françoise Willien , Sylvie Wolf

The Korteweg-de Vries equation is one of the most important nonlinear evolution equations in the mathematical sciences. In this article invariant discretization schemes are constructed for this equation both in the Lagrangian and in the…

Mathematical Physics · Physics 2015-06-22 Alexander Bihlo , Xavier Coiteux-Roy , Pavel Winternitz

This paper presents a novel approach for numerical solution of a class of fourth order time fractional partial differential equations (PDE's). The finite difference formulation has been used for temporal discretization, whereas, the space…

Numerical Analysis · Mathematics 2018-09-18 Muhammad Abbas

We present a natural framework for constructing energy-stable time discretization schemes. By leveraging the Onsager principle, we demonstrate its efficacy in formulating partial differential equation models for diverse gradient flow…

Numerical Analysis · Mathematics 2024-10-16 Huangxin Chen , Hailiang Liu , Xianmin Xu

In this paper we present numerical methods - finite differences and finite elements - for solution of partial differential equation of fractional order in time for one-dimensional space. This equation describes anomalous diffusion which is…

Mathematical Physics · Physics 2007-05-23 Mariusz Ciesielski , Jacek Leszczynski

This paper presents a family of spatial discretisations of the nonlinear rotating shallow-water equations that conserve both energy and potential enstrophy. These are based on two-dimensional mixed finite element methods and hence, unlike…

Numerical Analysis · Mathematics 2015-06-16 Andrew T. T. McRae , Colin J. Cotter

Physics-informed neural networks (PINNs) have gained significant attention for solving forward and inverse problems related to partial differential equations (PDEs). While advancements in loss functions and network architectures have…

Machine Learning · Computer Science 2025-08-11 Adrian Celaya , David Fuentes , Beatrice Riviere

Port-Hamiltonian systems provide a highly-structured framework for modeling of physical systems. By definition, they encode a balance equation relating energy changes to supplied and dissipated energy. Capturing this energy balance in…

Numerical Analysis · Mathematics 2026-05-15 Aashutosh Sharma , Andreas Bartel , Manuel Schaller

For a general class of nonlinear port-Hamiltonian systems we develop a high-order time discretization scheme with certain structure preservation properties. The finite or infinite-dimensional system under consideration possesses a…

Numerical Analysis · Mathematics 2024-07-23 Jan Giesselmann , Attila Karsai , Tabea Tscherpel

The Adomian decomposition method is a semi-analytical method for solving ordinary and partial nonlinear differential equations. The aim of this paper is to apply Adomian decomposition method to obtain approximate solutions of nonlinear…

Numerical Analysis · Mathematics 2017-12-27 Iqra Javed , Ashfaq Ahmad , Muzammil Hussain , S. Iqbal

Solving analytically intractable partial differential equations (PDEs) that involve at least one variable defined on an unbounded domain arises in numerous physical applications. Accurately solving unbounded domain PDEs requires efficient…

Machine Learning · Computer Science 2026-05-12 Mingtao Xia , Lucas Böttcher , Tom Chou

In multi-phase fluid flow, fluid-structure interaction, and other applications, partial differential equations (PDEs) often arise with discontinuous coefficients and singular sources (e.g., Dirac delta functions). These complexities arise…

Numerical Analysis · Mathematics 2019-07-24 Chung-Nan Tzou , Samuel Stechmann