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Generalized Additive Runge-Kutta schemes have shown to be a suitable tool for solving ordinary differential equations with additively partitioned right-hand sides. This work develops symplectic GARK schemes for additively partitioned…

Numerical Analysis · Mathematics 2023-12-14 Michael Günther , Adrian Sandu , Kevin Schäfers , Antonella Zanna

This work generalizes the additively partitioned Runge-Kutta methods by allowing for different stage values as arguments of different components of the right hand side. An order conditions theory is developed for the new family of…

Numerical Analysis · Computer Science 2013-10-22 Adrian Sandu , Michael Guenther

This work considers multirate generalized-structure additively partitioned Runge-Kutta (MrGARK) methods for solving stiff systems of ordinary differential equations (ODEs) with multiple time scales. These methods treat different partitions…

Numerical Analysis · Mathematics 2022-01-19 Steven Roberts , John Loffeld , Arash Sarshar , Carol S. Woodward , Adrian Sandu

This work constructs a new class of multirate schemes based on the recently developed generalized additive Runge-Kutta (GARK) methods (Sandu and Guenther, 2013). Multirate schemes use different step sizes for different components and for…

Numerical Analysis · Computer Science 2013-10-24 Michael Guenther , Adrian Sandu

We consider a Lagrangian system $L(q,\dot q) = \sum_{l=1}^{N}L^{\{l\}}(q,\dot q)$, where the $q$-variable is treated by a Generalized Additive Runge--Kutta (GARK) method. Applying the technique of discrete variations, we show how to…

Numerical Analysis · Mathematics 2020-08-05 Antonella Zanna

Systems driven by multiple physical processes are central to many areas of science and engineering. Time discretization of multiphysics systems is challenging, since different processes have different levels of stiffness and characteristic…

Numerical Analysis · Mathematics 2022-01-19 Adrian Sandu , Michael Günther , Steven Roberts

Splitting-based time integration approaches such as fractional steps, alternating direction implicit, operator splitting, and locally one-dimensional methods partition the system of interest into components and solve individual components…

Hamiltonian systems are one of the most important class of dynamical systems with a geometric structure called symplecticity and the numerical algorithms which can preserve such geometric structure are of interest. In this article we study…

Numerical Analysis · Mathematics 2015-10-16 Wensheng Tang , Guangming Lang , Xuqiong Luo

We show that symplectic Runge-Kutta methods provide effective symplectic integrators for Hamiltonian systems with index one constraints. These include the Hamiltonian description of variational problems subject to position and velocity…

Numerical Analysis · Mathematics 2014-02-28 Robert I McLachlan , Klas Modin , Olivier Verdier , Matt Wilkins

We study the problem of efficient integration of variational equations in multi-dimensional Hamiltonian systems. For this purpose, we consider a Runge-Kutta-type integrator, a Taylor series expansion method and the so-called `Tangent Map'…

Chaotic Dynamics · Physics 2016-12-21 Enrico Gerlach , Siegfried Eggl , Charalampos Skokos

A novel symplectic integrator for Hamiltonian equations on $S_2^n \times T^{\ast} \RR^m$ is developed and studied. Partitioned Runge--Kutta methods for Hamiltonian systems on products of Hamiltionian manifolds are studied, specifically,…

Numerical Analysis · Mathematics 2018-09-18 Geir Bogfjellmo

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 construct stochastic symplectic Runge--Kutta (SSRK) methods of high strong order for Hamiltonian systems with additive noise. By means of colored rooted tree theory, we combine conditions of mean-square order 1.5 and…

Numerical Analysis · Mathematics 2017-05-24 Weien Zhou , Jingjing Zhang , Jialin Hong , Songhe Song

This paper develops a general methodology for a posteriori error estimation in time-dependent multiphysics numerical simulations. The methodology builds upon the generalized-structure additive Runge--Kutta (GARK) approach to time…

Numerical Analysis · Mathematics 2020-01-27 Mahesh Narayanamurthi , Ulrich Römer , Adrian Sandu

We reconsider the variational derivation of symplectic partitioned Runge-Kutta schemes. Such type of variational integrators are of great importance since they integrate mechanical systems with high order accuracy while preserving the…

Numerical Analysis · Mathematics 2015-05-08 Cédric M. Campos

We are concerned with the efficient implementation of symplectic implicit Runge-Kutta (IRK) methods applied to systems of (non-necessarily Hamiltonian) ordinary differential equations by means of Newton-like iterations. We pay particular…

Numerical Analysis · Mathematics 2017-03-23 Mikel Antoñana , Joseba Makazaga , Ander Murua

This work focuses on the development of a new class of high-order accurate methods for multirate time integration of systems of ordinary differential equations. Unlike other recent work in this area, the proposed methods support mixed…

Numerical Analysis · Mathematics 2023-01-04 Rujeko Chinomona , Daniel R. Reynolds

Symplectic partitioned Runge--Kutta methods can be obtained from a variational formulation where all the terms in the discrete Lagrangian are treated with the same quadrature formula. We construct a family of symplectic methods allowing the…

Numerical Analysis · Mathematics 2019-09-25 Antonella Zanna

Differential equations arising in many practical applications are characterized by multiple time scales. Multirate time integration seeks to solve them efficiently by discretizing each scale with a different, appropriate time step, while…

Numerical Analysis · Computer Science 2022-02-03 Adrian Sandu

When applied to stiff, linear differential equations with time-dependent forcing, Runge-Kutta methods can exhibit convergence rates lower than predicted by the classical order condition theory. Commonly, this order reduction phenomenon is…

Numerical Analysis · Mathematics 2022-02-15 Steven Roberts , Adrian Sandu
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