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Here we implement the Azencott method to prove the moderate deviation principle for the two-dimensional incompressible stochastic Navier-Stokes equations in a bounded domain. As applications two types of the law of the iterated logarithm…

Probability · Mathematics 2022-02-18 Parisa Fatheddin

A Freidlin-Wentzell type large deviation principle is established for stochastic partial differential equations with slow and fast time-scales, where the slow component is a one-dimensional stochastic Burgers equation with small noise and…

Probability · Mathematics 2020-03-10 Xiaobin Sun , Ran Wang , Lihu Xu , Xue Yang

In this paper, we establish a large deviation principle for stochastic evolution equations with reflection in an infinite dimensional ball. Weak convergence approach plays an important role.

Probability · Mathematics 2024-03-05 Zdzisław Brzeźniak , Qi Li , Tusheng Zhang

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

We investigate an anisotropic vanishing viscosity limit of the 3D stochastic Navier-Stokes equations posed between two horizontal plates, with Dirichlet no-slip boundary condition. The turbulent viscosity is split into horizontal and…

Probability · Mathematics 2026-03-18 Daniel Goodair

In this paper we show the strong convergence of a fully explicit space-time discrete approximation scheme for the solution process of the two-dimensional incompressible stochastic Navier-Stokes equations on the torus driven by additive…

Probability · Mathematics 2018-09-07 Sara Mazzonetto

This paper investigates the Cauchy problem for the barotropic compressible Navier-Stokes equations in $\mathbb{R}^2$ with the constant state as far field, which may be vacuum or non-vacuum. Under the assumption of a sufficiently large bulk…

Analysis of PDEs · Mathematics 2026-01-27 Qinghao Lei , Chengfeng Xiong

We examine the large-time behavior of axisymmetric solutions without swirl of the Navier--Stokes equation in $\mathbb{R}^3$. We construct higher-order asymptotic expansions for the corresponding vorticity. The appeal of this work lies in…

Analysis of PDEs · Mathematics 2023-11-08 Christian Seis , Dominik Winkler

Loosely speaking, the Navier-Stokes-$\alpha$ model and the Navier-Stokes equations differ by a spatial filtration parametrized by a scale denoted $\alpha$. Starting from a strong two-dimensional solution to the Navier-Stokes-$\alpha$ model…

Analysis of PDEs · Mathematics 2022-10-06 Jad Doghman , Ludovic Goudenège

We study the motion of a particle in a random time-dependent vector field defined by the 2D Navier-Stokes system with a noise. Under suitable non-degeneracy hypotheses we prove that the empirical measures of the trajectories of the pair…

Mathematical Physics · Physics 2019-02-12 Vojkan Jaksic , Vahagn Nersesyan , Claude-Alain Pillet , Armen Shirikyan

We consider the compressible Navier-Stokes equation with density dependent viscosity coefficients, focusing on the case where those coefficients vanish on vacuum. We prove the stability of weak solutions both in the torus and in the whole…

Analysis of PDEs · Mathematics 2007-05-23 Antoine Mellet , Alexis F. Vasseur

The asymptotic analysis of a class of stochastic partial differential equations (SPDEs) with fully locally monotone coefficients covering a large variety of physical systems, a wide class of quasilinear SPDEs and a good number of fluid…

Probability · Mathematics 2022-12-13 Ankit Kumar , Manil T. Mohan

In this paper, we study the asymptotic behavior of randomly perturbed path-dependent stochastic differential equations with small parameter $\vartheta_{\varepsilon}$, when $\varepsilon \rightarrow 0$, $\vartheta_\varepsilon$ goes to $0$.…

Probability · Mathematics 2023-04-03 Liu Xiangdong , Hong Shaopeng

Recently, for periodic initial data with initial density allowed to vanish, Huang and Li [1] establish the global existence of strong and weak solutions for the two-dimensional compressible Navier{Stokes equations with no restrictions on…

Analysis of PDEs · Mathematics 2012-06-21 Fei Jiang

This work aims to prove the small time large deviation principle (LDP) for a class of stochastic partial differential equations (SPDEs) with locally monotone coefficients in generalized variational framework. The main result could be…

Probability · Mathematics 2021-02-23 Shihu Li , Wei Liu , Yingchao Xie

In this paper we study the Large Deviation Principle (LDP in abbreviation) for a class of Stochastic Partial Differential Equations (SPDEs) in the whole space $\mathbb{R}^d$, with arbitrary dimension $d\geq 1$, under random influence which…

Probability · Mathematics 2015-05-20 Tarik El Mellali , Mohamed Mellouk

We prove a large deviation principle result for solutions of abstract stochastic evolution equations perturbed by small Levy noise. We use general large deviations theorems of Varadhan and Bryc, viscosity solutions of integro-partial…

Probability · Mathematics 2010-03-09 Andrzej Swiech , Jerzy Zabczyk

The main goal of this article is to study the effect of small, highly nonlinear, unbounded drifts (small time large deviation principle (LDP) based on exponential equivalence arguments) for a class of stochastic partial differential…

Probability · Mathematics 2022-12-27 Ankit Kumar , Manil T. Mohan

The stability of solutions under periodic perturbations for both inviscid and viscous conservation laws is an interesting and important problem. In this paper, a large-amplitude viscous shock under space-periodic perturbation for the…

Analysis of PDEs · Mathematics 2021-09-15 Feimin Huang , Qian Yuan

In this paper, we revisit the joint low-Mach and low-Frode number limit for the compressible Navier-Stokes equations with degenerate, density-dependent viscosity. Employing the relative entropy framework based on the concept of…

Analysis of PDEs · Mathematics 2025-12-01 Nilasis Chaudhuri , Francesco Fanelli , Yang Li , Ewelina Zatorska
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