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The intention of this article is to illustrate the use of methods from symplectic geometry for practical purposes. Our intended audience is scientists interested in orbits of Hamiltonian systems (e.g. the three-body problem). The main…

Symplectic Geometry · Mathematics 2023-03-10 Urs Frauenfelder , Dayung Koh , Agustin Moreno

Mapping methods, including the Meyer-Miller-Stock-Thoss (MMST) mapping and spin-mapping, are commonly utilised to simulate nonadiabatic dynamics by propagating classical mapping variable trajectories. Recent work confirmed the Momentum…

Chemical Physics · Physics 2026-03-13 Lauren E. Cook , James R. Rampton , Timothy J. H. Hele

Symplectic integrators are the tool of choice for many researchers studying dynamical systems because of their good long-term energy conservation properties. For systems with a dominant central mass, symplectic integrators are also highly…

Astrophysics · Physics 2007-05-23 John E. Chambers

We establish the geometric ergodicity of the preconditioned Hamiltonian Monte Carlo (HMC) algorithm defined on an infinite-dimensional Hilbert space, as developed in [Beskos et al., Stochastic Process. Appl., 2011]. This algorithm can be…

Statistics Theory · Mathematics 2020-03-19 Nathan E. Glatt-Holtz , Cecilia F. Mondaini

The general applicability and ease of use of the pseudo-marginal Metropolis--Hastings (PMMH) algorithm, and particle Metropolis--Hastings in particular, makes it a popular method for inference on discretely observed Markovian stochastic…

Statistics Theory · Mathematics 2024-11-19 Chris Sherlock

This article is a survey of classical and quantum completely integrable systems from the viewpoint of local ``phase space'' analysis. It advocates the use of normal forms and shows how to get global information from glueing local pieces.…

Analysis of PDEs · Mathematics 2007-05-23 San Vu Ngoc

The trapezoidal rule, which is a special case of the Newmark family of algorithms, is one of the most widely used methods for transient hyperbolic problems. In this work, we show that this rule conserves linear and angular momenta and…

Computational Physics · Physics 2014-01-07 Arup Kumar Nandy , C. S. Jog

Second-order Volume-preserving algorithms (VPAs) for simulating charged particle motion in electromagnetic fields have been generalized to a rotating angle formulation by using the matrix decomposition methods. Based on this method, the…

Plasma Physics · Physics 2025-03-25 Jian Wang , Xiaodong Zhang , Lei Ye , Xingyuan Xu

This paper presents stability and accuracy analysis of a high-order explicit time stepping scheme introduced by \cite[Section 2.2]{Buvoli2019}, which exhibits superior stability compared to classical Adams-Bashforth. A conjecture that is…

Numerical Analysis · Mathematics 2026-04-01 Daopeng Yin , Liquan Mei

We introduce a new definition of discrete-time port-Hamiltonian systems (PHS), which results from structure-preserving discretization of explicit PHS in time. We discretize the underlying continuous-time Dirac structure with the collocation…

Dynamical Systems · Mathematics 2018-11-20 Paul Kotyczka , Laurent Lefèvre

The Kerr-MOG black hole has recently attracted significant research attention and has been extensively applied in various fields. To accurately characterize the long-term dynamical evolution of charged particles around Kerr-MOG black hole,…

General Relativity and Quantum Cosmology · Physics 2025-07-22 Zhen-Meng Xu , Da-Zhu Ma , Wen-Fu Cao , Kai Li

We describe a method for symplectic tracking of charged particles through static electric and magnetic fields. The method can be applied to cases where the fields have a dependence on longitudinal as well as transverse position, and where…

Accelerator Physics · Physics 2018-08-22 Andrzej Wolski , Alex Herrod

In this article, we first introduce the notion of a {\it continuous cover} of a manifold parametrised by any compact manifold endowed with a mass 1 volume-form. We prove that any such cover admits a partition of unity where the usual sum is…

Symplectic Geometry · Mathematics 2019-01-25 François Lalonde , Jordan Payette

Geometric numerical integration has recently been exploited to design symplectic accelerated optimization algorithms by simulating the Lagrangian and Hamiltonian systems from the variational framework introduced in Wibisono et al. In this…

Optimization and Control · Mathematics 2023-05-19 Valentin Duruisseaux , Melvin Leok

The dynamics of attractive bosons trapped in one dimensional anharmonic potentials is investigated. Particular emphasis is put on the variance of the position and momentum many-particle operators. Coupling of the center-of-mass and…

Quantum Gases · Physics 2018-12-18 Ofir E. Alon , Lorenz S. Cederbaum

The equations of motion of a mechanical system subjected to nonholonomic linear constraints can be formulated in terms of a linear almost Poisson structure in a vector bundle. We study the existence of invariant measures for the system in…

Mathematical Physics · Physics 2015-02-23 Yuri N. Fedorov , Luis C. García-Naranjo , Juan C. Marrero

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

Reduced magnetohydrodynamics is a simplified set of magnetohydrodynamics equations with applications to both fusion and astrophysical plasmas, possessing a noncanonical Hamiltonian structure and consequently a number of conserved…

Computational Physics · Physics 2017-10-05 Michael Kraus , Emanuele Tassi , Daniela Grasso

Many definitions of moist potential vorticity (PV) have been proposed to extend the dry theory of Ertel PV. None of the moist PV definitions seem to have all of the desirable properties of the dry Ertel PV. For instance, dry PV is not only…

Atmospheric and Oceanic Physics · Physics 2023-05-17 Parvathi Kooloth , Leslie M. Smith , Samuel N. Stechmann

Inductive bias refers to restrictions on the hypothesis class that enable a learning method to generalize effectively from limited data. A canonical example in control is linearity, which underpins low sample-complexity guarantees for…

Optimization and Control · Mathematics 2026-04-21 Zhuo Ouyang , Jixian Liu , Enrique Mallada