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相关论文: A high-order explicit Runge-Kutta approximation te…

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In this paper, we present a comprehensive long-time stability analysis of a second-order explicit exponential Runge--Kutta (ERK2) method for the Cahn--Hilliard (CH) equation. By employing Fourier spectral collocation in space and a…

数值分析 · 数学 2025-12-08 Jing Guo

Fully implicit Runge-Kutta (IRK) methods have many desirable accuracy and stability properties as time integration schemes, but high-order IRK methods are not commonly used in practice with large-scale numerical PDEs because of the…

数值分析 · 数学 2021-10-07 Ben S. Southworth , Oliver Krzysik , Will Pazner

In this paper, two new families of fourth-order explicit exponential Runge--Kutta (ERK) methods with four stages are studied for solving first-order differential systems $y'(t)+My(t)=f(y(t))$. By comparing the Taylor series of the exact…

数值分析 · 数学 2024-06-19 Xianfa Hu , Yonglei Fang , Bin Wang

The ability to accurately approximate trajectories of dynamical systems enables their analysis, prediction, and control. Neural network (NN)-based approximations have attracted significant interest due to fast evaluation with good accuracy…

计算物理 · 物理学 2024-01-11 Jochen Stiasny , Spyros Chatzivasileiadis

A semi-implicit-explicit (semi-IMEX) Runge-Kutta (RK) method is proposed for the numerical integration of ordinary differential equations (ODEs) of the form $\mathbf{u}' = \mathbf{f}(t,\mathbf{u}) + G(t,\mathbf{u}) \mathbf{u}$, where…

数值分析 · 数学 2025-04-15 Lingyun Ding

The aim of this paper is to construct and analyze explicit exponential Runge-Kutta methods for the temporal discretization of linear and semilinear integro-differential equations. By expanding the errors of the numerical method in terms of…

数值分析 · 数学 2023-01-24 Alexander Ostermann , Fardin Saedpanah , Nasrin Vaisi

Extended Stability Runge-Kutta (ESRK) methods are crucial for solving large-scale computational problems in science and engineering, including weather forecasting, aerodynamic analysis, and complex biological modelling. However, balancing…

机器学习 · 计算机科学 2025-06-27 Gavin Lee Goodship , Luis Miralles-Pechuan , Stephen O'Sullivan

Variational space-time formulations for Partial Differential Equations have been of great interest in the last decades. While it is known that implicit time marching schemes have variational structure, the Galerkin formulation of explicit…

数值分析 · 数学 2018-06-21 Judit Muñoz-Matute , David Pardo , Victor M. Calo , Elisabete Alberdi

In this paper, we develop a general framework for constructing higher-order, unconditionally energy-stable exponential time differencing Runge-Kutta methods applicable to a range of gradient flows. Specifically, we identify conditions…

数值分析 · 数学 2024-07-23 Zhaohui Fu , Jie Shen , Jiang Yang

We propose a practical implementation of high-order fully implicit Runge-Kutta(IRK) methods in a multiple precision floating-point environment. Although implementations based on IRK methods in an IEEE754 double precision environment have…

数值分析 · 数学 2013-06-18 Tomonori Kouya

A novel class of explicit high-order energy-preserving methods are proposed for general Hamiltonian partial differential equations with non-canonical structure matrix. When the energy is not quadratic, it is firstly done that the original…

数值分析 · 数学 2020-06-02 Chaolong Jiang , Yushun Wang , Yuezheng Gong

A wide range of physical phenomena exhibit auxiliary admissibility criteria, such as conservation of entropy or various energies, which arise implicitly under exact solution of their governing PDEs. However, standard temporal schemes, such…

数值分析 · 数学 2025-03-27 Mohammad R. Najafian , Brian C. Vermeire

We propose a unified theoretical framework to examine the energy dissipation properties at all stages of explicit exponential Runge-Kutta (EERK) methods for gradient flow problems. The main part of the novel framework is to construct the…

数值分析 · 数学 2024-10-10 Hong-lin Liao , Xuping Wang

Using a recent characterization of energy-preserving B-series, we derive the explicit conditions on the coefficients of a Runge-Kutta method that ensure energy preservation (for Hamiltonian systems) up to a given order in the step size,…

数值分析 · 数学 2025-01-24 Gabriel A. Barrios de León , David I. Ketcheson , Hendrik Ranocha

We introduce a new class of Runge-Kutta type methods suitable for time stepping to propagate hyperbolic solutions within tent-shaped spacetime regions. Unlike standard Runge-Kutta methods, the new methods yield expected convergence…

数值分析 · 数学 2020-02-28 Jay Gopalakrishnan , Joachim Schöberl , Christoph Wintersteiger

When evolving in time the solution of a hyperbolic partial differential equation, it is often desirable to use high order strong stability preserving (SSP) time discretizations. These time discretizations preserve the monotonicity…

数值分析 · 数学 2017-08-02 Sidafa Conde , Sigal Gottlieb , Zachary J. Grant , John N. Shadid

In this paper, a family of arbitrarily high-order structure-preserving exponential Runge-Kutta methods are developed for the nonlinear Schr\"odinger equation by combining the scalar auxiliary variable approach with the exponential…

数值分析 · 数学 2020-09-15 Jin Cui , Zhuangzhi Xu , Yushun Wang , Chaolong Jiang

In this paper, exponential Runge-Kutta methods of collocation type (ERKC) which were originally proposed in (Appl Numer Math 53:323-339, 2005) are extended to semilinear parabolic problems with time-dependent delay. Two classes of the ERKC…

数值分析 · 数学 2025-12-30 Qiumei Huang , Alexander Ostermann , Gangfan Zhong

We show in this paper that third- and fourth-order low storage Runge-Kutta algorithms can be built specifically for quadratic nonlinear operators, at the expense of roughly doubling the time needed for evaluating the temporal derivatives.…

流体动力学 · 物理学 2008-08-14 Marc E. Brachet , Pablo D. Mininni , Duane L. Rosenberg , Annick Pouquet

The paper aims at developing low-storage implicit Runge-Kutta methods which are easy to implement and achieve higher-order of convergence for both the velocity and pressure in the finite volume formulation of the incompressible…

数值分析 · 数学 2019-07-08 Jiawei Wan , Ahsan Kareem , Haili Liao , Yunzhu Cai