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We prove the exponential convergence to a unique invariant measure for locally damped nonlinear Schr\"odinger equations, perturbed by bounded noise acting on only two Fourier modes. To tackle the lack of smoothing effect, we introduce…

Analysis of PDEs · Mathematics 2026-04-08 Yuxuan Chen , Shengquan Xiang , Zhifei Zhang

We establish a new criterion for exponential mixing of random dynamical systems. Our criterion is applicable to a wide range of systems, including in particular dispersive equations. Its verification is in nature related to several topics,…

Analysis of PDEs · Mathematics 2024-07-23 Ziyu Liu , Dongyi Wei , Shengquan Xiang , Zhifei Zhang , Jia-Cheng Zhao

We study a damped stochastic non-linear Schr\"{o}dinger (NLS) equation driven by an additive noise. It is white in time and smooth in space. Using a coupling method, we establish convergence of the Markovian transition semi-group toward a…

Analysis of PDEs · Mathematics 2007-05-23 Arnaud Debussche , Cyril Odasso

We study the ergodicity of finite-dimensional approximations of the Schr\"odinger equation. The system is driven by a multiplicative scalar noise. Under general assumptions over the distribution of the noise, we show that the system has a…

Mathematical Physics · Physics 2007-10-22 Vahagn Nersesyan

We study the long-time mixing behavior of the stochastic nonlinear Schr\"odinger equation in $\mathbb{R}^d$, $d\le 3$. It is well known that, under a sufficiently strong damping force, the system admits unique ergodicity, although the rate…

Probability · Mathematics 2026-01-01 Hung D. Nguyen , Kihoon Seong

We study a class of discrete-time random dynamical systems with compact phase space. Assuming that the deterministic counterpart of the system in question possesses a dissipation property, its linearisation is approximately controllable,…

Analysis of PDEs · Mathematics 2019-10-30 Sergei Kuksin , Vahagn Nersesyan , Armen Shirikyan

We provide deterministic controllability conditions that imply exponential mixing properties for randomly forced constrained dynamical systems with possibly unbounded state space. As an application, new ergodicity results are obtained for…

Optimization and Control · Mathematics 2025-11-07 Laurent Mertz , Vahagn Nersesyan , Manuel Rissel

This paper is devoted to proving the polynomial mixing for a weakly damped stochastic nonlinear Schr\"{o}dinger equation with additive noise on a 1D bounded domain. The noise is white in time and smooth in space. We consider both focusing…

Probability · Mathematics 2023-04-03 Jing Guo , Zhenxin Liu

We study the nonlinear Schr\"odinger equation with linear damping, i.e. a zero order dissipation, and additive noise. Working in $R^d$ with d = 2 or d = 3, we prove the uniqueness of the invariant measure when the damping coefficient is…

Probability · Mathematics 2022-05-27 Zdzislaw Brzezniak , Benedetta Ferrario , Margherita Zanella

The ergodic properties of the randomly forced Navier-Stokes system have been extensively studied in the literature during the last two decades. The problem has always been considered in bounded domains, in order to have, for example,…

Analysis of PDEs · Mathematics 2019-03-04 Vahagn Nersesyan

In this paper, we study the defocusing nonlinear Schr\"{o}dinger equation with a locally distributed damping on a smooth bounded domain as well as on the whole space and on an exterior domain. We first construct approximate solutions using…

We study stochastic partial differential equations of the reaction-diffusion type. We show that, even if the forcing is very degenerate (i.e. has not full rank), one has exponential convergence towards the invariant measure. The convergence…

Mathematical Physics · Physics 2009-11-07 Martin Hairer

We consider parabolic stochastic partial differential equations driven by white noise in time. We prove exponential convergence of the transition probabilities towards a unique invariant measure under suitable conditions. These conditions…

Probability · Mathematics 2007-05-23 Martin Hairer

In this paper, we investigate the convergence order in probability of a novel ergodic numerical scheme for damped stochastic nonlinear Schr\"{o}dinger equation with an additive noise. Theoretical analysis shows that our scheme is of order…

Numerical Analysis · Mathematics 2016-11-29 Jialin Hong , Lihai Ji , Xu Wang

We prove existence and uniqueness of the invariant measure and exponential mixing in the total-variation norm for a class of stochastic differential equations driven by degenerate compound Poisson processes. In addition to mild assumptions…

Probability · Mathematics 2022-09-21 Vahagn Nersesyan , Renaud Raquépas

We establish the existence, uniqueness and exponential attraction properties of an invariant measure for the MHD equations with degenerate stochastic forcing acting only in the magnetic equation. The central challenge is to establish time…

Probability · Mathematics 2020-03-17 Xuhui Peng , Jianhua Huang , Yan Zheng

This paper studies the 1D stochastic Allen--Cahn equation on a bounded domain driven by localized white noise. We prove that the associated Markov process admits a unique invariant measure and is exponential mixing. The main challenge lies…

Probability · Mathematics 2026-05-08 Ziyu Liu , Shengquan Xiang , Zhifei Zhang

We consider a finite dimensional approximation of the stochastic nonlinear Schr\"odinger equation driven by multiplicative noise, which is derived by applying a symplectic method to the original equation in spatial direction. Both the…

Numerical Analysis · Mathematics 2016-11-29 Jialin Hong , Xu Wang , Liying Zhang

We establish a general criterion which ensures exponential mixing of parabolic Stochastic Partial Differential Equations (SPDE) driven by a non additive noise which is white in time and smooth in space. We apply this criterion on two…

Analysis of PDEs · Mathematics 2007-05-23 Cyril Odasso

We prove that well posed quasilinear equations of parabolic type, perturbed by bounded nondegenerate random forces, are exponentially mixing for a large class of random forces.

Mathematical Physics · Physics 2019-09-26 Sergei Kuksin , Huilin Zhang
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