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We propose efficient numerical methods for nonseparable non-canonical Hamiltonian systems which are explicit, K-symplectic in the extended phase space with long time energy conservation properties. They are based on extending the original…

Numerical Analysis · Mathematics 2023-03-01 Beibei Zhu , Lun Ji , Aiqing Zhu , Yifa Tang

Based on a variational principle with a stochastic forcing, we indicate that the stochastic Schr\"odinger equation in Stratonovich sense is an infinite-dimensional stochastic Hamiltonian system, whose phase flow preserves symplecticity. We…

Numerical Analysis · Mathematics 2016-05-05 Chuchu Chen , Jialin Hong

This letter studies symmetric and symplectic exponential integrators when applied to numerically computing nonlinear Hamiltonian systems. We first establish the symmetry and symplecticity conditions of exponential integrators and then show…

Numerical Analysis · Mathematics 2018-12-11 Yajun Wu , Bin Wang

We prove that the recently developed semiexplicit symplectic integrators for non-separable Hamiltonian systems preserve any linear and quadratic invariants possessed by the Hamiltonian systems. This is in addition to being symmetric and…

Numerical Analysis · Mathematics 2023-06-06 Tomoki Ohsawa

The paper concerns semidiscretizations in time of stochastic Maxwell equations driven by additive noise. We show that the equations admit physical properties and mathematical structures, including regularity, energy and divergence evolution…

Numerical Analysis · Mathematics 2018-06-07 Chuchu Chen , Jialin Hong , Lihai Ji

In this paper, we propose an efficient exponential integrator finite element method for solving a class of semilinear parabolic equations in rectangular domains. The proposed method first performs the spatial discretization of the model…

Numerical Analysis · Mathematics 2022-09-27 Jianguo Huang , Lili Ju , Yuejin Xu

In this paper, we consider stochastic Runge-Kutta methods for stochastic Hamiltonian partial differential equations and present some sufficient conditions for multisymplecticity of stochastic Runge-Kutta methods of stochastic Hamiltonian…

Symplectic Geometry · Mathematics 2018-03-02 Liying Zhang , Lihai Ji

In this work, we develop a class of up to third-order energy-stable schemes for the Cahn--Hilliard equation. Building on Lawson's integrating factor Runge--Kutta method, which is widely used for stiff semilinear equations, we discuss its…

Numerical Analysis · Mathematics 2024-11-26 Haifeng Wang , Jingwei Sun , Hong Zhang , Xu Qian , Songhe Song

We consider a wide class of semi linear Hamiltonian partial differential equa- tions and their approximation by time splitting methods. We assume that the nonlinearity is polynomial, and that the numerical tra jectory remains at least uni-…

Numerical Analysis · Mathematics 2009-12-16 Erwan Faou , Benoit Grebert

Explicit and semi-explicit geometric integration schemes for dissipative perturbations of Hamiltonian systems are analyzed. The dissipation is characterized by a small parameter $\epsilon$, and the schemes under study preserve the…

Numerical Analysis · Mathematics 2015-03-19 Klas Modin , Gustaf Söderlind

In this paper, we present an error analysis of one-stage explicit extended Runge--Kutta--Nystr\"{o}m integrators for semilinear wave equations. These equations are analysed by using spatial semidiscretizations with periodic boundary…

Numerical Analysis · Mathematics 2018-09-18 Bin Wang , Xinyuan Wu

Structure-preserving linearly implicit exponential integrators are constructed for Hamiltonian partial differential equations with linear constant damping. Linearly implicit integrators are derived by polarizing the polynomial terms of the…

Numerical Analysis · Mathematics 2024-03-19 Murat Uzunca , Bülent Karasözen

Due to the nonseparability of the post-Newtonian (PN) Hamiltonian systems of compact objects, the symplectic methods that admit the linear error growth and the near preservation of first integrals are always implicit as explicit symplectic…

Instrumentation and Methods for Astrophysics · Physics 2024-10-10 Shixiang Huang , Kaiming Zeng , Xinghua Niu , Lijie Mei

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…

Numerical Analysis · Mathematics 2025-12-08 Jing Guo

This paper focuses on the numerical approximation of the linearized shallow water equations using hybridizable discontinuous Galerkin (HDG) methods, leveraging the Hamiltonian structure of the evolution system. First, we propose an…

Numerical Analysis · Mathematics 2025-07-04 C. Núñez , M. A. Sánchez

The success of symplectic integrators for Hamiltonian ODEs has led to a decades-long program of research seeking analogously structure-preserving numerical methods for Hamiltonian PDEs. In this paper, we construct a large class of such…

Numerical Analysis · Mathematics 2026-01-05 Ari Stern , Enrico Zampa

In the last few decades, numerical simulation for nonlinear oscillators has received a great deal of attention, and many researchers have been concerned with the design and analysis of numerical methods for solving oscillatory problems. In…

Numerical Analysis · Mathematics 2020-12-25 Yu-Wen Li , Xinyuan Wu

We consider the construction of semi-implicit linear multistep methods which can be applied to time dependent PDEs where the separation of scales in additive form, typically used in implicit-explicit (IMEX) methods, is not possible. As…

Numerical Analysis · Mathematics 2020-01-14 Giacomo Albi , Lorenzo Pareschi

This work establishes a rigorous connection between stability properties of discrete-time algorithms (DTAs) and corresponding continuous-time dynamical systems derived through $ O(s^r) $-resolution ordinary differential equations (ODEs). We…

Optimization and Control · Mathematics 2026-03-03 Amir Ali Farzin , Yuen-Man Pun , Philipp Braun , Iman Shames

In this work, a kernel-based surrogate for integrating Hamiltonian dynamics that is symplectic by construction and tailored to large prediction horizons is proposed. The method learns a scalar potential whose gradient enters a…

Numerical Analysis · Mathematics 2026-01-27 Robin Herkert , Tobias Ehring , Bernard Haasdonk