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In this article, we show how the theory of rough paths can be used to provide a notion of solution to a class of nonlinear stochastic PDEs of Burgers type that exhibit too high spatial roughness for classical analytical methods to apply. In…

Probability · Mathematics 2010-08-11 Martin Hairer

The solution of a parabolic stochastic partial differential equation (SPDE) driven by an infinite-dimensional Brownian motion is in general not a semi-martingale anymore and does in general not satisfy an It\^{o} formula like the solution…

Probability · Mathematics 2010-10-04 Arnulf Jentzen , Peter Kloeden

We show existence and pathwise uniqueness of probabilistically strong solutions to a pseudomonotone stochastic evolution problem on a bounded domain $D\subseteq\mathbb{R}^d$, $d\in\mathbb{N}$, with homogeneous Dirichlet boundary conditions…

Probability · Mathematics 2024-03-19 Kerstin Schmitz , Aleksandra Zimmermann

We study lattice approximations of reflected stochastic elliptic equations driven by white noise on a bounded domain in $\mathbb{R}^d,\ d=1,2,3$. The convergence of the scheme is established.

Numerical Analysis · Mathematics 2018-08-01 Jun Dai , Jing Zhang

The celebrated De Giorgi-Nash-Moser theory ensures that solutions to uniformly elliptic or parabolic PDEs are bounded and H\"older continuous, even with merely bounded measurable coefficients. For parabolic SPDEs with transport noise,…

Probability · Mathematics 2025-11-18 Antonio Agresti , Max Sauerbrey , Mark Veraar

Recently, a solution theory for one-dimensional stochastic PDEs of Burgers type driven by space-time white noise was developed. In particular, it was shown that natural numerical approximations of these equations converge and that their…

Probability · Mathematics 2016-05-20 Martin Hairer , Konstantin Matetski

It is frequently the case that a white-noise-driven parabolic and/or hyperbolic stochastic partial differential equation (SPDE) can have random-field solutions only in spatial dimension one. Here we show that in many cases, where the…

Probability · Mathematics 2007-11-14 Mohammud Foondun , Davar Khoshnevisan , Eulalia Nualart

Using probabilistic methods, we establish a-priori estimates for two classes of quasilinear parabolic systems of partial differential equations (PDEs). We treat in particular the case of a nonlinearity which has quadratic growth in the…

Probability · Mathematics 2023-04-05 Joe Jackson

This article proposes for stochastic partial differential equations (SPDEs) driven by additive noise, a novel approach for the approximate parameterizations of the ``small'' scales by the ``large'' ones, along with the derivaton of the…

Analysis of PDEs · Mathematics 2013-11-14 Mickaël D. Chekroun , Honghu Liu , Shouhong Wang

We prove a maximum principle for local solutions of quasilinear stochastic PDEs with obstacle (in short OSPDE). The proofs are based on a version of It\^o's formula and estimates for the positive part of a local solution which is…

Probability · Mathematics 2013-04-17 Denis Laurent , Matoussi Anis , Zhang Jing

We investigate, in the setting of UMD Banach spaces E, the continuous dependence on the data A, F, G and X_0 of mild solutions of semilinear stochastic evolution equations with multiplicative noise of the form dX(t) = [AX(t) + F(t,X(t))]dt…

Probability · Mathematics 2011-02-10 Markus Kunze , Jan van Neerven

We consider stochastic semilinear partial differential equations with Lipschitz nonlinear terms. We prove existence and uniqueness of an invariant measure and the existence of a solution for the corresponding Kolmogorov equation in the…

Probability · Mathematics 2007-05-23 Luigi Manca

In this note we introduce a new approach to rough and stochastic partial differential equations (RPDEs and SPDEs): we consider general Banach spaces as state spaces and -- for the sake of simiplicity -- finite dimensional sources of noise,…

Probability · Mathematics 2009-08-21 Josef Teichmann

In this paper we develop a new approach to nonlinear stochastic partial differential equations with Gaussian noise. Our aim is to provide an abstract framework which is applicable to a large class of SPDEs and includes many important cases…

Functional Analysis · Mathematics 2022-05-02 Antonio Agresti , Mark Veraar

We consider a system of semilinear partial differential equations (PDEs) with a nonlinearity depending on both the solution and its gradient. The Neumann boundary condition depends on the solution in a nonlinear manner. The uniform…

Probability · Mathematics 2022-01-14 Khaled Bahlali , Brahim Boufoussi , Soufiane Mouchtabih

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 are interested in path-dependent stochastic differential equations (SDEs) which are controlled by Brownian motion and its delays. Within this non-Markovian context, we give a H \"ormander-type criterion for the regularity…

Probability · Mathematics 2020-09-17 Reda Chhaibi , Ibrahim Ekren

These notes are based on a series of lectures given first at the University of Warwick in spring 2008 and then at the Courant Institute, Imperial College London, and EPFL. It is an attempt to give a reasonably self-contained presentation of…

Probability · Mathematics 2023-07-04 Martin Hairer

We study mild solutions of a class of stochastic partial differential equations, involving operators with polynomially bounded coefficients. We consider semilinear equations under suitable hyperbolicity hypotheses on the linear part. We…

Analysis of PDEs · Mathematics 2018-09-27 Alessia Ascanelli , Sandro Coriasco , André Süß

We consider nonlinear parabolic stochastic PDEs on a bounded Lipschitz domain driven by a Gaussian noise that is white in time and colored in space, with Dirichlet or Neumann boundary condition. We establish existence, uniqueness and moment…

Probability · Mathematics 2023-08-07 David Candil , Le Chen , Cheuk Yin Lee
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