English
Related papers

Related papers: Effective dynamics of stochastic wave equation wit…

200 papers

A microscopic heterogeneous system under random influence is considered. The randomness enters the system at physical boundary of small scale obstacles as well as at the interior of the physical medium. This system is modeled by a…

Analysis of PDEs · Mathematics 2009-11-13 Wei Wang , Jinqiao Duan

An effective macroscopic model for a stochastic microscopic system is derived. The original microscopic system is modeled by a stochastic partial differential equation defined on a domain perforated with small holes or heterogeneities. The…

Analysis of PDEs · Mathematics 2007-05-23 Wei Wang , Daomin Cao , Jinqiao Duan

In order to understand the impact of random influences at physical boundary on the evolution of multiscale systems, a stochastic partial differential equation model under a fast random dynamical boundary condition is investigated. The…

Dynamical Systems · Mathematics 2008-08-07 Wei Wang , Jinqiao Duan

In the present work, we investigate the dynamics of the infinite-dimensional stochastic partial differential equation (SPDE) with multiplicative white noise. We derive the effective equation on the approximate slow manifold in detail by…

Dynamical Systems · Mathematics 2025-05-08 Shenglan Yuan , Dirk Blömker

This article investigates an energy balance model coupled to the primitive equations by a dynamic boundary condition with and without noise on the boundary. It is shown that this system is globally strongly well-posed both in the…

Analysis of PDEs · Mathematics 2025-10-29 Gianmarco Del Sarto , Matthias Hieber , Tarek Zöchling

This work is concerned with a singularly perturbed stochastic nonlinear wave equation with a random dynamical boundary condition. A splitting skill is used to derive the approximating equation of the system in the sense of probability…

Analysis of PDEs · Mathematics 2012-08-30 Guanggan Chen , Jinqiao Duan , Jian Zhang

Constructing numerical models of noisy partial differential equations is very delicate. Our long term aim is to use modern dynamical systems theory to derive discretisations of dissipative stochastic partial differential equations. As a…

Dynamical Systems · Mathematics 2007-05-23 A. J. Roberts

In this work we study permanence of hyperbolicity for autonomous differential equations under nonautonomous random/stochastic perturbations. For the linear case, we study robustness and existence of exponential dichotomies for nonautonomous…

Analysis of PDEs · Mathematics 2021-04-06 Tomás Caraballo , Alexandre N. de Carvalho , José A. Langa , Alexandre N. Oliveira-Sousa

The influence of small random perturbations on a deterministic dynamical system with a locally stable equilibrium is considered. The perturbed system is described by the It\^{o} stochastic differential equation. It is assumed that the noise…

Mathematical Physics · Physics 2016-02-18 Oskar Sultanov

Dynamical systems theory provides powerful methods to extract effective macroscopic dynamics from complex systems with slow modes and fast modes. Here we derive and theoretically support a macroscopic, spatially discrete, model for a class…

Analysis of PDEs · Mathematics 2010-03-12 Wei Wang , A. J. Roberts

In this paper, we develop a way of analyzing the random dynamics of stochastic evolution equations with a non-dense domain. Such problems cover several types of evolution equations. We are particularly interested in evolution equations with…

Probability · Mathematics 2024-10-28 M. Ghani Varzaneh , F. Z. Lahbiri , S. Riedel

We study linear damped and viscoelastic wave equations evolving on a bounded domain. For both models, we assume that waves are subject to an inhomogeneous Neumann boundary condition on a portion of the domain's boundary. The analysis of…

Analysis of PDEs · Mathematics 2024-10-15 Türker Özsarı , İdem Susuzlu

The long term aim is to use modern dynamical systems theory to derive discretisations of noisy, dissipative partial differential equations. As a first step we here consider a small domain and apply stochastic centre manifold techniques to…

Dynamical Systems · Mathematics 2025-10-20 A. J. Roberts

This paper investigates the well-posedness and small-noise asymptotics of a class of stochastic partial differential equations defined on a bounded domain of $\mathbb{R}^d$, where the diffusion coefficient depends nonlinearly and…

Probability · Mathematics 2025-06-23 Sandra Cerrai , Giuseppina Guatteri , Gianmario Tessitore

This paper concerns the dynamical behaviors of acoustic wave motion driven by a force acting through the boundary. If the boundary force is a suitable control, we show that the dynamical system associated to the acoustic wave motion is…

Analysis of PDEs · Mathematics 2025-08-26 Zhe Jiao , Xiao Li , Qin Zhao

In this paper, we investigate the wave solutions of a stochastic rotating shallow water model. This approximate model provides an interesting simple description of the interplay between waves and random forcing ensuing either from the wind…

Fluid Dynamics · Physics 2023-05-02 Etienne Mémin , Long Li , Noé Lahaye , Gilles Tissot , Bertrand Chapron

We consider the effect of replacing in stochastic differential equations leading to the dynamical collapse of the statevector, white noise stochastic processes with non white ones. We prove that such a modification can be consistently…

Quantum Physics · Physics 2009-11-07 Angelo Bassi , GianCarlo Ghirardi

We consider weakly damped nonlinear Schr\"odinger equations perturbed by a noise of small amplitude. The small noise is either complex and of additive type or real and of multiplicative type. It is white in time and colored in space. Zero…

Numerical Analysis · Mathematics 2009-07-19 Eric Gautier

Multiscale modelling aims to systematically construct macroscale models of materials with fine microscale structure. However, macroscale boundary conditions are typically not systematically derived, but rely on heuristic arguments,…

Dynamical Systems · Mathematics 2019-11-18 Chen Chen , A. J. Roberts , J. E. Bunder

We present a Bayesian non-parametric way of inferring stochastic differential equations for both regression tasks and continuous-time dynamical modelling. The work has high emphasis on the stochastic part of the differential equation, also…

Machine Learning · Statistics 2020-06-29 Martin Jørgensen , Marc Peter Deisenroth , Hugh Salimbeni
‹ Prev 1 2 3 10 Next ›