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We study the Navier-Stokes equations with transport noise in critical function spaces. Assuming the initial data belongs to $H^{1/2}$ almost surely, we establish the existence and uniqueness of a local-in-time probabilistically strong…

Probability · Mathematics 2025-11-07 Mustafa Sencer Aydın , Fanhui Xu

We address the local well-posedness for the stochastic Navier-Stokes system with multiplicative cylindrical noise in the whole space. More specifically, we prove that there exists a unique local strong solution to the system in…

Analysis of PDEs · Mathematics 2023-01-31 Igor Kukavica , Fei Wang , Fanhui Xu

We address the global-in-time existence and pathwise uniqueness of solutions for the stochastic incompressible Navier-Stokes equations with a multiplicative noise on the three-dimensional torus. Under natural smallness conditions on the…

Probability · Mathematics 2024-10-07 Igor Kukavica , Fanhui Xu

We address the global existence of solutions to the stochastic Navier-Stokes equations with multiplicative noise and with initial data in $H^{1/2}(\mathbb{T}^{3})$. We prove that the solution exists globally in time with probability…

Probability · Mathematics 2025-01-20 Mustafa Sencer Aydın , Igor Kukavica , Fanhui Xu

This paper considers the supercritical Navier-Stokes equations posed in the whole space $\R^d$, with suitably randomized initial data, in the weak solution setting. The global weak solutions are constructed for a large set of initial data…

Analysis of PDEs · Mathematics 2013-10-29 Robin Ming Chen , Dehua Wang , Song Yao , Cheng Yu

For the stochastic Navier-Stokes equations with a multiplicative white noise on ${\mathbb T}^{3}$, we prove that there exists a unique strong solution locally in time when the initial datum belongs to $L^p(\Omega; L^p)$ for $p>3$.

Analysis of PDEs · Mathematics 2021-10-15 Igor Kukavica , Fanhui Xu

Considering the stochastic Navier-Stokes system in $\mathbb{R}^d$ forced by a multiplicative white noise, we establish the local existence and uniqueness of the strong solution when the initial data take values in the critical space…

Analysis of PDEs · Mathematics 2017-12-07 Lihuai Du , Ting Zhang

We study the theory of local and global strong solution for the stochastic tamed Navier--Stokes equations with multiplicative Wiener and L\'evy jump noise in the whole space $\R^3$. More specifically, we first prove the existence of a…

Analysis of PDEs · Mathematics 2026-05-06 Bikram Podder , Surendra Kumar

The aim of the note is to discuss different definitions of solutions to the Cauchy problem for the Navier-Stokes equations with the initial data belonging to the Lebesgue space $L_3(\mathbb R^3)$

Analysis of PDEs · Mathematics 2016-01-14 G. Seregin , V. Sverak

We prove short time regularity of suitable weak solutions of 3D incompressible Navier-Stokes equations near a point where the initial data is locally in $L^3$. The result is applied to the regularity problems of solutions with uniformly…

Analysis of PDEs · Mathematics 2018-12-31 Kyungkeun Kang , Hideyuki Miura , Tai-Peng Tsai

We are concerned with the three dimensional navier-stokes equations driven by a general multiplicative noise. For every divergence free and mean free initial condition in L2, we establish existence of infinitely many global-in-time…

Probability · Mathematics 2025-05-13 Huaxiang Lv , Yichun Zhu

We construct a local in time spatially real-analytic solution to the 2D and 3D stochastic Navier--Stokes equation driven by a spatially real-analytic multiplicative and transport noise but emanating from an initial condition that is only…

Analysis of PDEs · Mathematics 2024-07-15 Dan Crisan , Prince Romeo Mensah

We consider the Navier-Stokes equations in vorticity form in $\mathbb{R}^2$ with a white noise forcing term of multiplicative type, whose spatial covariance is not regular enough to apply the It\^o calculus in $L^q$ spaces, $1<q<\infty$. We…

Probability · Mathematics 2018-03-06 Benedetta Ferrario , Margherita Zanella

In this paper we introduce a probabilistic approach to show the existence of initial data with arbitrarily large $L^2(\mathbb{R}^3)$, $\dot{H}^{1/2}(\mathbb{R}^3)$ and $\mathcal{PM}^2$-norms for which a Generalized Navier-Stokes system…

Analysis of PDEs · Mathematics 2011-09-12 Jean C. Cortissoz

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 propose and study a temporal, and spatio-temporal discretisation of the 2D stochastic Navier--Stokes equations in bounded domains supplemented with no-slip boundary conditions. Considering additive noise, we base its construction on the…

Numerical Analysis · Mathematics 2022-03-23 Dominic Breit , Andreas Prohl

We review some basic results on existence and uniqueness of the invariant measure for the two-dimensional stochastic Navier-Stokes equations. A large part of the literature concerns the additive noise case; after revising these models, we…

Probability · Mathematics 2025-01-06 Benedetta Ferrario , Margherita Zanella

We show that the classical Cauchy problem for the incompressible 3d Navier-Stokes equations with $(-1)$-homogeneous initial data has a global scale-invariant solution which is smooth for positive times. Our main technical tools are…

Analysis of PDEs · Mathematics 2012-04-04 Hao Jia , Vladimír Šverák

In this paper, we investigate the existence of a unique global smooth solution to the three-dimensional incompressible Navier-Stokes equations and provide a concise proof. We establish a new global well-posedness result that allows the…

Analysis of PDEs · Mathematics 2025-03-03 Haina Li , Yiran Xu

In this paper we study the stochastic Navier-Stokes equations on the $d$-dimensional torus with transport noise, which arise in the study of turbulent flows. Under very weak smoothness assumptions on the data we prove local well-posedness…

Analysis of PDEs · Mathematics 2023-12-12 Antonio Agresti , Mark Veraar
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