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Related papers: A note on continuous-stage Runge-Kutta methods

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We combine the recent relaxation approach with multiderivative Runge-Kutta methods to preserve conservation or dissipation of entropy functionals for ordinary and partial differential equations. Relaxation methods are minor modifications of…

Numerical Analysis · Mathematics 2024-06-19 Hendrik Ranocha , Jochen Schütz

Relaxation Runge-Kutta methods reproduce a fully discrete dissipation (or conservation) of entropy for entropy stable semi-discretizations of nonlinear conservation laws. In this paper, we derive the discrete adjoint of relaxation…

Numerical Analysis · Mathematics 2021-07-27 Mario J. Bencomo , Jesse Chan

Singly-TASE operators for the numerical solution of stiff differential equations were proposed by Calvo et al. in J.Sci. Comput. 2023 to reduce the computational cost of Runge-Kutta-TASE (RKTASE) methods when the involved linear systems are…

Numerical Analysis · Mathematics 2024-07-03 M. Calvo , J. I. Montijano , L. Rández

A numerical search approach is used to design high-order diagonally implicit Runge-Kutta (DIRK) schemes equipped with embedded error estimators, some of which have identical diagonal elements (SDIRK) and explicit first stage (ESDIRK). In…

Numerical Analysis · Mathematics 2023-09-12 Yousef Alamri , David I. Ketcheson

Many important initial value problems have the property that energy is non-increasing in time. Energy stable methods, also referred to as strongly stable methods, guarantee the same property discretely. We investigate requirements for…

Numerical Analysis · Mathematics 2020-11-26 Hendrik Ranocha , David I. Ketcheson

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…

Numerical Analysis · Mathematics 2024-07-23 Zhaohui Fu , Jie Shen , Jiang Yang

This paper investigates the competitiveness of semi-implicit Runge-Kutta (RK) and spectral deferred correction (SDC) time-integration methods up to order six for incompressible Navier-Stokes problems in conjunction with a high-order…

Numerical Analysis · Mathematics 2022-10-03 Montadhar Guesmi , Martina Grotteschi , Jörg Stiller

Constructing explicit Runge--Kutta (ERK) methods with as few stages as possible for a given order is a classical problem in numerical analysis. In this work, we introduce a $Q$/$D$-space framework of sufficient order conditions for ERK…

Numerical Analysis · Mathematics 2026-05-19 Junyuan He , Jizu Huang

This paper investigates, a new class of fractional order Runge-Kutta (FORK) methods for numerical approximation to the solution of fractional differential equations (FDEs). By using the Caputo generalizedTaylor formula and the total…

Numerical Analysis · Mathematics 2023-03-06 F. Ghoreishi , R. Ghaffari

The purpose of the article is to derive equations that determine the trajectory of a non-conservative natural system in configuration space in non-stationary external fields. A theorem on the change in the kinetic energy of the system is…

General Physics · Physics 2025-09-12 V. Voytik

Runge--Kutta (RK) methods are widely used techniques for solving a class of initial value problems. In this article, we introduce an adaptive multiquadratic (MQ) radial basis function (RBF)-based method to develop enhanced explicit RK…

Numerical Analysis · Mathematics 2025-07-08 Rajesh Yadav , Deepak Kumar Yadav , Alpesh Kumar

In the present paper, a class of stochastic Runge-Kutta methods containing the second order stochastic Runge-Kutta scheme due to E. Platen for the weak approximation of It\^o stochastic differential equation systems with a multi-dimensional…

Numerical Analysis · Mathematics 2013-03-20 Kristian Debrabant , Andreas Rößler

Modified Patankar-Runge-Kutta (MPRK) schemes are numerical methods for the solution of positive and conservative production-destruction systems. They adapt explicit Runge-Kutta schemes to ensure positivity and conservation irrespective of…

Numerical Analysis · Mathematics 2017-03-16 Stefan Kopecz , Andreas Meister

This paper presents high-order Runge-Kutta (RK) discontinuous Galerkin methods for the Euler-Poisson equations in spherical symmetry. The scheme can preserve a general polytropic equilibrium state and achieve total energy conservation up to…

Numerical Analysis · Mathematics 2022-06-15 Weijie Zhang , Yulong Xing , Eirik Endeve

A mixed accuracy framework for Runge--Kutta methods presented in [Grant, JSC 2022] has been shown to speed up the computation in diagonally implicit Runge--Kutta (DIRK) methods by using less expensive low accuracy approaches for the…

Numerical Analysis · Mathematics 2026-04-29 John Driscoll , Sigal Gottlieb , Zachary J. Grant , César Herrera , Tej Sai Kakumanu , Monica Stephens

Irksome is a library based on the Unified Form Language (UFL) that automates the application of Runge-Kutta time-stepping methods for finite element spatial discretizations of partial differential equations (PDEs). This paper describes…

Numerical Analysis · Mathematics 2025-08-29 Robert C. Kirby , Scott P. MacLachlan , Pablo D. Brubeck

Nonlinear parabolic equations are central to numerous applications in science and engineering, posing significant challenges for analytical solutions and necessitating efficient numerical methods. Exponential integrators have recently…

Numerical Analysis · Mathematics 2024-12-24 Trung Hau Hoang

This paper analyses the long-time behaviour of one-stage symplectic or symmetric extended Runge--Kutta--Nystr\"{o}m (ERKN) methods when applied to nonlinear wave equations. It is shown that energy, momentum, and all harmonic actions are…

Numerical Analysis · Mathematics 2018-10-04 Bin Wang , Xinyuan Wu

This work focuses on the numerical study of a recently published class of Runge-Kutta methods designed for mixed-precision arithmetic. We employ the methods in solving partial differential equations on modern hardware. In particular we…

Numerical Analysis · Mathematics 2024-12-24 Ivo Dravins , Marcel Koch , Victoria Griehl , Katharina Kormann

Neural networks have proven to be efficient surrogate models for tackling partial differential equations (PDEs). However, their applicability is often confined to specific PDEs under certain constraints, in contrast to classical PDE solvers…

Machine Learning · Computer Science 2023-10-03 Hoyun Choi , Sungyeop Lee , B. Kahng , Junghyo Jo