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Nonlinear dynamical systems possessing reflection symmetry have an invariant subspace in the phase space. The dynamics within the invariant subspace can be random or chaotic. As a system parameter changes, the motion transverse to the…

Chaotic Dynamics · Physics 2007-05-23 Changsong Zhou , C. -H. Lai

We take into account the dynamics of three types of models of rotating galaxies in polar coordinates in a rotating frame. Due to non-axisymmetric potential perturbations, the angular momentum varies with time, and the kinetic energy depends…

Computational Physics · Physics 2023-02-14 Li-Na Zhang , Wen-Fang Liu , Xin Wu

Although stochastic optimization is central to modern machine learning, the precise mechanisms underlying its success, and in particular, the precise role of the stochasticity, still remain unclear. Modelling stochastic optimization…

Machine Learning · Statistics 2020-06-12 Liam Hodgkinson , Michael W. Mahoney

A universal symplectic structure for a Newtonian system including nonconservative cases can be constructed in the framework of Birkhoffian generalization of Hamiltonian mechanics. In this paper the symplectic geometry structure of…

Mathematical Physics · Physics 2018-01-17 Hongling Su , Mengzhao Qin

In this work we consider a class of stochastic parabolic equations with singular space depending potential, random driving force and random initial condition. For the analysis of these equations we combine the chaos expansion method from…

Analysis of PDEs · Mathematics 2021-09-15 Snežana Gordić , Tijana Levajković , Ljubica Oparnica

A deterministic-stochastic coupling scheme is developed for simulating rarefied gas flows, where the key process is the alternative solving of the macroscopic synthetic equations [Su et al., J. Comput. Phys., 407 (2020) 109245] and the…

Computational Physics · Physics 2024-06-26 Liyan Luo , Qi Li , Fei Fei , Lei Wu

We present a reduced basis stochastic Galerkin method for partial differential equations with random inputs. In this method, the reduced basis methodology is integrated into the stochastic Galerkin method, resulting in a significant…

Numerical Analysis · Mathematics 2023-10-02 Guanjie Wang , Qifeng Liao

We consider the generalized almost periodic homogenization problem for two different types of stochastic conservation laws with oscillatory coefficients and multiplicative noise. In both cases the stochastic perturbations are such that the…

Analysis of PDEs · Mathematics 2022-07-08 Hermano Frid , Kenneth H. Karlsen , Daniel Marroquin

Based on the physics of stochastic processes we present a new approach for structural health monitoring. We show that the new method allows for an in-situ analysis of the elastic features of a mechanical structure even for realistic…

Data Analysis, Statistics and Probability · Physics 2013-01-08 Philip Rinn , Hendrik Heißelmann , Matthias Wächter , Joachim Peinke

This work is devoted to deriving small mass limiting equation for a class of Hamiltonian systems with multiplicative L\'evy noise. Derivation of the limiting equation depends on the structure of the stochastic Hamiltonian systems, in which…

Probability · Mathematics 2021-05-18 Zibo Wang , Li Lv , Jinqiao Duan

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 aim of the present paper is to provide necessary and sufficient conditions to maintain a stochastic coupled system, with porous media components and gradient-type noise in a prescribed set of constraints by using internal controls. This…

Analysis of PDEs · Mathematics 2022-02-08 Ioana Ciotir , Dan Goreac , Ionut Munteanu

This paper is a survey of methods for solving smooth (strongly) monotone stochastic variational inequalities. To begin with, we give the deterministic foundation from which the stochastic methods eventually evolved. Then we review methods…

Optimization and Control · Mathematics 2023-04-04 Aleksandr Beznosikov , Boris Polyak , Eduard Gorbunov , Dmitry Kovalev , Alexander Gasnikov

Randomized compilation protocols have recently attracted attention as alternatives to traditional deterministic Trotter-Suzuki methods, potentially reducing circuit depth and resource overhead. These protocols determine gate application…

Quantum Physics · Physics 2025-12-22 Yun-Zhuo Fan , Yu-Xia Wu , Dan-Bo Zhang

We use the global stochastic analysis tools introduced by P. A. Meyer and L. Schwartz to write down a stochastic generalization of the Hamilton equations on a Poisson manifold that, for exact symplectic manifolds, are characterized by a…

Probability · Mathematics 2007-10-08 Joan-Andreu Lázaro-Camí , Juan-Pablo Ortega

In this paper, we introduce concepts of pathwise random almost periodic and almost automorphic solutions for dynamical systems generated by non-autonomous stochastic equations. These solutions are pathwise stochastic analogues of…

Dynamical Systems · Mathematics 2014-05-27 Bixiang Wang

This work is about parameter estimation for a fast-slow stochastic system with non-Gaussian $\alpha$-stable L\'evy noise. When the observations are only available for slow components, a system parameter is estimated and the accuracy for…

Dynamical Systems · Mathematics 2020-02-28 Ying Chao , Pingyuan Wei , Jinqiao Duan

We analyze the application to elastodynamic problems of mixed finite element methods for elasticity with weak symmetry. Our approach leads to a semidiscrete method which consists of a system of ordinary differential equations without…

Numerical Analysis · Mathematics 2018-11-13 Douglas N. Arnold , Jeonghun J. Lee

Noisy fluctuations are ubiquitous in complex systems. They play a crucial or delicate role in the dynamical evolution of gene regulation, signal transduction, biochemical reactions, among other systems. Therefore, it is essential to…

Dynamical Systems · Mathematics 2018-11-05 Jinqiao Duan , Hui Wang

Many applications, such as optimization, uncertainty quantification and inverse problems, require repeatedly performing simulations of large-dimensional physical systems for different choices of parameters. This can be prohibitively…

Machine Learning · Computer Science 2023-12-18 Benedikt Brantner , Michael Kraus