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It is widely believed that at high Reynolds number (Re) all turbulent flows approach a state of "fully developed turbulence" defined by a unique, Re-independent statistics of the velocity fluctuations. Yet direct measurements of the…

Fluid Dynamics · Physics 2009-11-11 G. Gioia , Pinaki Chakraborty , Fabián A. Bombardelli

This work establishes the existence and regularity of random pullback attractors for parabolic partial differential equations with rough nonlinear multiplicative noise under natural assumptions on the coefficients. To this aim, we combine…

Probability · Mathematics 2024-01-26 Alexandra Neamtu , Tim Seitz

A stochastic linear transport equation with multiplicative noise is considered and the question of no-blow-up is investigated. The drift is assumed only integrable to a certain power. Opposite to the deterministic case where smooth initial…

Probability · Mathematics 2013-03-19 Ennio Fedrizzi , Franco Flandoli

Within the rough path framework we prove the continuity of the solution to random differential equations driven by fractional Brownian motion with respect to the Hurst parameter $H$ when $H \in (1/3, 1/2]$.

Probability · Mathematics 2024-08-27 Francesco C. De Vecchi , Luca M. Giordano , Daniela Morale , Stefania Ugolini

We present a derivation of a stochastic model of Navier Stokes equations that relies on a decomposition of the velocity fields into a differentiable drift component and a time uncorrelated uncertainty random term. This type of decomposition…

Fluid Dynamics · Physics 2015-06-15 Etienne Mémin

In this paper, we study a multidimensional backward stochastic differential equation (BSDE) with an additional rough drift (rough BSDE), and give the existence and uniqueness of the adapted solution, either when the terminal value and the…

Probability · Mathematics 2024-01-12 Jiahao Liang , Shanjian Tang

In this paper we study rough differential equations driven by Gaussian rough paths from the viewpoint of Malliavin calculus. Under mild assumptions on coefficient vector fields and underlying Gaussian processes, we prove that solutions at a…

Probability · Mathematics 2014-06-09 Yuzuru Inahama

We consider differential equations driven by rough paths and study the regularity of the laws and their long time behavior. In particular, we focus on the case when the driving noise is a rough path valued fractional Brownian motion with…

Probability · Mathematics 2013-07-25 Martin Hairer , Natesh S. Pillai

We consider a stochastic flow on $\mathds{R}$ generated by an SDE with its drift being a function of bounded variation. We show that the flow is differentiable with respect to the initial conditions. Asymptotic properties of the flow are…

Probability · Mathematics 2014-04-10 Olga V. Aryasova , Andrey Yu. Pilipenko

We consider anticipative Stratonovich stochastic differential equations driven by some stochastic process lifted to a rough path. Neither adaptedness of initial point and vector fields nor commuting conditions between vector field is…

Probability · Mathematics 2011-11-10 Laure Coutin , Peter Friz , Nicolas Victoir

In this note we consider differential equations driven by a signal $x$ which is $\gamma$-H\"older with $\gamma>1/3$, and is assumed to possess a lift as a rough path. Our main point is to obtain existence of solutions when the coefficients…

Probability · Mathematics 2017-08-17 Prakash Chakraborty , Samy Tindel

In this paper, we investigate two hyperbolic flows obtained by adding forcing terms in direction of the position vector to the hyperbolic mean curvature flows in \cite{klw,hdl}. For the first hyperbolic flow, as in \cite{klw}, by using…

Differential Geometry · Mathematics 2012-11-26 Jing Mao

It is well-known that a stochastic differential equation (SDE) on a Euclidean space driven by a Brownian motion with Lipschitz coefficients generates a stochastic flow of homeomorphisms. When the coefficients are only locally Lipschitz,…

Probability · Mathematics 2016-05-09 Xue-Mei Li , Michael Scheutzow

We establish the existence of solutions to path-dependent rough differential equations with non-anticipative coefficients. Regularity assumptions on the coefficients are formulated in terms of horizontal and vertical derivatives.

Probability · Mathematics 2020-01-30 Anna Ananova

We prove a large deviation principle for the slow-fast rough differential equations under the controlled rough path framework. The driver rough paths are lifted from the mixed fractional Brownian motion with Hurst parameter $H\in…

Probability · Mathematics 2025-02-05 Xiaoyu Yang , Yong Xu

Using the multiple stochastic integrals we prove an existence and uniqueness result for a linear stochastic equation driven by the fractional Brownian motion with any Hurst parameter. We study both the one parameter and two parameter cases.…

Probability · Mathematics 2007-05-23 Ivan Nourdin , Ciprian A. Tudor

We prove a modification to the classical maximal inequality for stochastic convolutions in 2-smooth Banach spaces using the factorization method. This permits to study semilinear stochastic partial differential equations with unbounded…

Probability · Mathematics 2020-10-20 Florian Bechtold

In this work, we introduce a solution theory for scalar-valued rough differential equations driven by multi-indices rough paths. To achieve this task, we will show how the flow approach using the log-ODE method introduced by Bailleul fits…

Probability · Mathematics 2026-01-19 Carlo Bellingeri , Yvain Bruned , Yingtong Hou

We formulate the flow of thick fluids as evolution variational and quasi-variational inequalities, with a variable threshold on the absolute value of the deformation rate tensor. In the variational case, we show the existence and uniqueness…

Analysis of PDEs · Mathematics 2026-01-22 Jos\é Francisco Rodrigues , Lisa Santos

We first prove some general results on pathwise uniqueness, comparison property and existence of nonnegative strong solutions of stochastic equations driven by white noises and Poisson random measures. The results are then used to prove the…

Probability · Mathematics 2012-04-12 Donald A. Dawson , Zenghu Li
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