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Related papers: Large deviations principle for 2D Navier-Stokes eq…

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We consider the incompressible 2D Navier-Stokes equations on the torus, driven by a deterministic time periodic force and a noise that is white in time and degenerate in Fourier space. The main result is twofold. Firstly, we establish a…

Probability · Mathematics 2023-07-13 Rongchang Liu , Kening Lu

The purpose of this paper is to establish the Donsker-Varadhan type large deviations principle (LDP) for the two-dimensional stochastic Navier-Stokes system. The main novelty is that the noise is assumed to be highly degenerate in the…

Probability · Mathematics 2022-02-01 Vahagn Nersesyan , Xuhui Peng , Lihu Xu

We study the large deviations principle (LDP) of Donsker-Varadhan type for the white-forced Navier-Stokes system in a bounded domain. Under the assumption that the noise is non-degenerate, we establish level-2 and level-3 LDPs with rate…

Analysis of PDEs · Mathematics 2025-06-18 Meng Zhao

In this paper we establish the large deviation principle for the the two-dimensional stochastic Navier-Stokes equations with anisotropic viscosity both for small noise and for short time. The proof for large deviation principle is based on…

Probability · Mathematics 2020-06-01 Bingguang Chen , Xiangchan Zhu

We are dealing with the validity of a large deviation principle for the two-dimensional Navier-Stokes equation, with periodic boundary conditions, perturbed by a Gaussian random forcing. We are here interested in the regime where both the…

Probability · Mathematics 2016-03-09 Sandra Cerrai , Arnaud Debussche

We study large deviations from the invariant measure for nonlinear Schr\"odinger equations with colored noises on determining modes. The proof is based on a new abstract criterion, inspired by [V. Jak\v{s}i\'{c} et al., Comm. Pure Appl.…

Analysis of PDEs · Mathematics 2026-02-03 Yuxuan Chen , Shengquan Xiang

In this paper, we establish a large deviation principle for 2D stochastic Chemotaxis-Navier-Stokes equation perturbed by a small multiplicative noise. The main difficulties come from the lack of a suitable compact embedding into the space…

Probability · Mathematics 2024-06-25 Yunfeng Chen , Xuhui Peng , Jianliang Zhai

We prove here the validity of a large deviation principle for the family of invariant measures associated to a two dimensional Navier-Stokes equation on a torus, perturbed by a smooth additive noise.

Probability · Mathematics 2015-09-02 Zdzislaw Brzezniak , Sandra Cerrai

In this paper, we consider the large deviations of invariant measure for the 3D stochastic hyperdissipative Navier-Stokes equations driven by additive noise. The unique ergodicity of invariant measure as a preliminary result is proved using…

Analysis of PDEs · Mathematics 2023-07-11 Zhaoyang Qiu , Hui Liu , Chengfeng Sun

We study the two-dimensional incompressible Navier-Stokes equation on the torus, driven by Gaussian noise that is white in time and colored in space. We consider the case where the magnitude of the random forcing $\sqrt{\e}$ and its…

Probability · Mathematics 2021-01-01 Sandra Cerrai , Nicholas Paskal

The aim of this paper is threefold. Firstly, we prove the existence and the uniqueness of a global strong (in both the probabilistic and the PDE senses) $\mathrm{H}^{1}_2$-valued solution to the 2D stochastic Navier-Stokes equations (SNSEs)…

Probability · Mathematics 2021-10-06 Zdzislaw Brzezniak , Xuhui Peng , Jianliang Zhai

We consider the 2D stochastic Navier-Stokes equations driven by noise that has the regularity of space-time white noise but doesn't exactly coincide with it. We show that, provided that the intensity of the noise is sufficiently weak at…

Probability · Mathematics 2025-10-22 Martin Hairer , Wenhao Zhao

The paper is devoted to studying the asymptotics of the family $(\mu^\varepsilon)$ of stationary measures of the Markov process generated by the flow of stochastic 2D Navier-Stokes equation with smooth white noise. By using the large…

Analysis of PDEs · Mathematics 2016-02-23 Davit Martirosyan

In this paper we consider the Lagrangian Averaged Navier-Stokes Equations, also known as, LANS-$\alpha$ Navier-Stokes model on the two dimensional torus. We assume that the noise is a cylindrical Wiener process and its coefficient is…

Analysis of PDEs · Mathematics 2021-07-27 Z. I. Ali , P. A. Razafimandimby , T. A. Tegegn

We consider the incompressible 2D Navier-Stokes equations with periodic boundary conditions driven by a deterministic time periodic forcing and a degenerate stochastic forcing. We show that the system possesses a unique ergodic periodic…

Dynamical Systems · Mathematics 2021-05-04 Rongchang Liu , Kening Lu

In this note, we prove the large deviation principle for the 2D-fractional stochastic Navier-Stokes equation on the torus under the dissipation order $ \alpha \in [\frac43, 2]$.

Probability · Mathematics 2013-09-06 Latifa Debbi

We consider randomly forced 2D Navier-Stokes equations in a bounded domain with smooth boundary. It is assumed that the random perturba- tion is non-degenerate, and its law is periodic in time and has a support localised with respect to…

Analysis of PDEs · Mathematics 2011-10-05 Armen Shirikyan

We study a finite-element based space-time discretisation for the 2D stochastic Navier-Stokes equations in a bounded domain supplemented with no-slip boundary conditions. We prove optimal convergence rates in the energy norm with respect to…

Numerical Analysis · Mathematics 2022-10-06 Dominic Breit , Andreas Prohl

We develop a general method to prove the existence of spectral gaps for Markov semigroups on Banach spaces. Unlike most previous work, the type of norm we consider for this analysis is neither a weighted supremum norm nor an ${\L}^p$-type…

Probability · Mathematics 2009-09-29 Martin Hairer , Jonathan C. Mattingly

The dynamical large deviations principle for the three-dimensional incompressible Landau-Lifschitz-Navier-Stokes equations is shown, in the joint scaling regime of vanishing noise intensity and correlation length. This proves the…

Probability · Mathematics 2024-03-07 Benjamin Gess , Daniel Heydecker , Zhengyan Wu
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