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We consider new implicit-explicit (IMEX) Runge-Kutta methods for hyperbolic systems of conservation laws with stiff relaxation terms. The explicit part is treated by a strong-stability-preserving (SSP) scheme, and the implicit part is…

Numerical Analysis · Mathematics 2010-09-16 L. Pareschi , G. Russo

In the paper explicit functional continuous Runge-Kutta and Runge-Kutta-Nystr\"om methods for retarded functional differential equations are considered. New methods for first order equations as well as for second order equations of the…

Numerical Analysis · Mathematics 2018-06-25 Alexey S. Eremin

In this work we consider a mixed precision approach to accelerate the implemetation of multi-stage methods. We show that Runge-Kutta methods can be designed so that certain costly intermediate computations can be performed as a…

Numerical Analysis · Mathematics 2020-12-25 Zachary J. Grant

Based on reasonable testing model problems, we study the preservation by symplectic Runge-Kutta method (SRK) and symplectic partitioned Runge-Kutta method (SPRK) of structures for fixed points of linear Hamiltonian systems. The…

Numerical Analysis · Mathematics 2008-02-18 Xiaohua Ding , Hongyu Liu , Zaijiu Shang , Geng Sun , Lingshu Wang

In this paper, we present a novel class of high-order Runge--Kutta (RK) discontinuous Galerkin (DG) schemes for hyperbolic conservation laws. The new method extends beyond the traditional method of lines framework and utilizes…

Numerical Analysis · Mathematics 2024-02-26 Qifan Chen , Zheng Sun , Yulong Xing

We introduce a family of stochastic optimization methods based on the Runge-Kutta-Chebyshev (RKC) schemes. The RKC methods are explicit methods originally designed for solving stiff ordinary differential equations by ensuring that their…

Optimization and Control · Mathematics 2022-02-01 Tony Stillfjord , Måns Williamson

Simulation-based techniques such as variants of stochastic Runge-Kutta are the de facto approach for inference with stochastic differential equations (SDEs) in machine learning. These methods are general-purpose and used with parametric and…

Machine Learning · Computer Science 2021-11-01 Arno Solin , Ella Tamir , Prakhar Verma

It is well known that symplectic Runge-Kutta and Partitioned Runge-Kutta methods exactly preserve {\em quadratic} first integrals (invariants of motion) of the system being integrated. While this property is often seen as a mere curiosity…

Numerical Analysis · Mathematics 2015-06-22 J. M. Sanz-Serna

We present the formulation and optimization of a Runge-Kutta-type time-stepping scheme for solving the shallow water equations, aimed at substantially increasing the effective allowable time-step over that of comparable methods. This…

Numerical Analysis · Mathematics 2023-12-27 Jeremy R. Lilly , Darren Engwirda , Giacomo Capodaglio , Robert L. Higdon , Mark R. Petersen

The problem of solving stochastic differential-algebraic equations (SDAEs) of index one with a scalar driving Brownian motion is considered. Recently, the authors proposed a class of stiffly accurate stochastic Runge-Kutta (SRK) methods…

Numerical Analysis · Mathematics 2013-11-07 Dominique Küpper , Anne Kværnø , Andreas Rößler

We present an approach for the efficient implementation of self-adjusting multi-rate Runge-Kutta methods and we introduce a novel stability analysis, that covers the multi-rate extensions of all standard Runge-Kutta methods and allows to…

The smoothing procedure known as the gradient flow that suppresses ultraviolet fluctuations of gauge fields plays an important role in lattice gauge theory calculations. In particular, this procedure is often used for high-precision scale…

High Energy Physics - Lattice · Physics 2021-01-15 Alexei Bazavov , Thomas Chuna

Stabilized methods (also called Chebyshev methods) are explicit methods with extended stability domains along the negative real axis. These methods are intended for large mildly stiff problems, originating mainly from parabolic PDEs. In…

Numerical Analysis · Mathematics 2023-03-30 Andrew Moisa , Boris Faleichik

Additive Runge-Kutta methods designed for preserving highly accurate solutions in mixed-precision computation were proposed and analyzed in 4. These specially designed methods use reduced precision for the implicit computations and full…

Numerical Analysis · Mathematics 2022-12-23 Ben Burnett , Sigal Gottlieb , Zachary J. Grant

Direct shooting is an efficient method to solve numerical optimal control. It utilizes the Runge-Kutta scheme to discretize a continuous-time optimal control problem making the problem solvable by nonlinear programming solvers. However,…

Systems and Control · Electrical Eng. & Systems 2024-03-12 Jiawei Tang , Yuxing Zhong , Pengyu Wang , Xingzhou Chen , Shuang Wu , Ling Shi

This article extends the theory of classical finite-difference summation-by-parts (FD-SBP) time-marching methods to the generalized summation-by-parts (GSBP) framework. Dual-consistent GSBP time-marching methods are shown to retain: A and…

Numerical Analysis · Mathematics 2016-01-26 Pieter D. Boom , David W. Zingg

Multirate integration is an increasingly relevant tool that enables scientists to simulate multiphysics systems. Existing multirate methods are designed for equations whose fast and slow variables can be linearly separated using additive or…

Numerical Analysis · Mathematics 2025-04-07 Tommaso Buvoli , Brian K. Tran , Ben S. Southworth

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

In this work modified Patankar-Runge-Kutta (MPRK) schemes up to order four are considered and equipped with a dense output formula of appropriate accuracy. Since these time integrators are conservative and positivity preserving for any time…

Numerical Analysis · Mathematics 2025-01-24 Thomas Izgin

We further develop a simple modification of Runge--Kutta methods that guarantees conservation or stability with respect to any inner-product norm. The modified methods can be explicit and retain the accuracy and stability properties of the…

Numerical Analysis · Mathematics 2019-05-27 David I. Ketcheson
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