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This work deals with the numerical solution of systems of oscillatory second-order differential equations which often arise from the semi-discretization in space of partial differential equations. Since these differential equations exhibit…

Numerical Analysis · Mathematics 2024-10-29 Lidia Aceto , Fabio Durastante

Multi-symplectic integrators are typically regarded as a discretization of the Hamiltonian partial differential equations. This is due to the fact that, for generic finite-dimensional Hamiltonian systems, there exists only one independent…

Dynamical Systems · Mathematics 2025-02-07 A. V. Tsiganov

In the following, we discuss nonlinear simulations of nonlinear dynamical systems, which are applied in technical and biological models. We deal with different ideas to overcome the treatment of the nonlinearities and discuss a novel…

Numerical Analysis · Mathematics 2014-12-01 Juergen Geiser , Vahid Yaghoubi

Most numerical integration algorithms are not designed specifically for Hamiltonian systems and do not respect their characteristic properties, which include the preservation of phase space volume with time. This can lead to spurious…

Astrophysics · Physics 2015-06-24 David JD Earn

We introduce a novel numerical method to integrate partial differential equations representing the Hamiltonian dynamics of field theories. It is a multi-symplectic integrator that locally conserves the stress-energy tensor with an excellent…

Numerical Analysis · Mathematics 2017-02-23 Hugo Ricateau , Leticia F. Cugliandolo

The structure of symplectic integrators up to fourth-order can be completely and analytical understood when the factorization (split) coefficents are related linearly but with a uniform nonlinear proportional factor. The analytic form of…

Mathematical Physics · Physics 2009-11-11 Siu A. Chin

The $k$-symplectic structures appear in the geometric study of the partial differential equations of classical field theories. Meanwhile, we present a new application of the $k$-symplectic structures to investigate a type of systems of…

Mathematical Physics · Physics 2015-08-06 J. de Lucas , M. Tobolski , S. Vilariño

The asymptotic iteration method (AIM) is an iterative technique used to find exact and approximate solutions to second-order linear differential equations. In this work, we employed AIM to solve systems of two first-order linear…

Mathematical Physics · Physics 2009-01-15 Katherine M. Robertson , Nasser Saad

The explicit integrability of second order ordinary differential equations invariant under time-translation and rescaling is investigated. Quadratic systems generated from the linearisable version of this class of equations are analysed to…

Exactly Solvable and Integrable Systems · Physics 2007-05-23 Peter Leach , Spiros Cotsakis , George Flessas

Following on our previous work [S. Delong and B. E. Griffith and E. Vanden-Eijnden and A. Donev, Phys. Rev. E, 87(3):033302, 2013], we develop temporal integrators for solving Langevin stochastic differential equations that arise in…

Statistical Mechanics · Physics 2015-06-23 S. Delong , Y. Sun , B. E. Griffith , E. Vanden-Eijnden , A. Donev

We thoroughly investigate Discontinuous Galerkin (DG) discretizations as time integrators for second-order oscillatory systems, considering both second-order and first-order formulations of the original problem. Key contributions include…

Numerical Analysis · Mathematics 2025-05-12 Gabriele Ciaramella , Martin J. Gander , Ilario Mazzieri

In this paper, we systematically construct two classes of structure-preserving schemes with arbitrary order of accuracy for canonical Hamiltonian systems. The one class is the symplectic scheme, which contains two new families of…

Numerical Analysis · Mathematics 2023-07-27 Yonghui Bo , Wenjun Cai , Yushun Wang

While symplectic integration methods based on operator splitting are well established in many branches of science, high order methods for Hamiltonian systems that split in more than two parts have not been studied in great detail. Here, we…

Computational Physics · Physics 2015-06-15 Ch. Skokos , E. Gerlach , J. D. Bodyfelt , G. Papamikos , S. Eggl

We implement and investigate the numerical properties of a new family of integrators connecting both variants of the symplectic Euler schemes, and including an alternative to the classical symplectic mid-point scheme, with some additional…

Numerical Analysis · Mathematics 2015-08-14 Hugo Jiménez-Pérez , Jean-Pierre Vilotte , Barbara Romanowicz

Complex-linearization of a class of systems of second order ordinary differential equations (ODEs) has already been studied with complex symmetry analysis. Linearization of this class has been achieved earlier by complex method, however,…

Classical Analysis and ODEs · Mathematics 2016-10-31 Hina M. Dutt , M. Safdar

Classical (maximal) superintegrable systems in $n$ dimensions are Hamiltonian systems with $2n-1$ independent constants of the motion, globally defined, the maximum number possible. They are very special because they can be solved…

Mathematical Physics · Physics 2015-11-04 Yuxuan Chen , Ernie G. Kalnins , Qiushi Li , Willard Miller

We establish the solvability of second order divergence type parabolic systems in Sobolev spaces. The leading coefficients are assumed to be only measurable in one spatial direction on each small parabolic cylinder with the spatial…

Analysis of PDEs · Mathematics 2011-03-01 Hongjie Dong , Doyoon Kim

Second-order superintegrable systems in dimensions two and three are essentially classified. With increasing dimension, however, the non-linear partial differential equations employed in current methods become unmanageable. Here we propose…

Differential Geometry · Mathematics 2025-05-09 Jonathan Kress , Konrad Schöbel , Andreas Vollmer

Motivated by the problem of solving the Einstein equations, we discuss high order finite difference discretizations of first order in time, second order in space hyperbolic systems.Particular attention is paid to the case when first order…

General Relativity and Quantum Cosmology · Physics 2010-01-18 M. Chirvasa , S. Husa

Optimization tasks are crucial in statistical machine learning. Recently, there has been great interest in leveraging tools from dynamical systems to derive accelerated and robust optimization methods via suitable discretizations of…

Statistical Mechanics · Physics 2023-07-06 Guilherme França , Alessandro Barp , Mark Girolami , Michael I. Jordan