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Related papers: H\"ormander's theorem for semilinear SPDEs

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This paper studies Liouville properties for viscosity sub- and supersolutions of fully nonlinear degenerate elliptic PDEs, under the main assumption that the operator has a family of generalized subunit vector fields that satisfy the…

Analysis of PDEs · Mathematics 2020-06-12 Martino Bardi , Alessandro Goffi

We generalize Lyons' rough paths theory in order to give a pathwise meaning to some nonlinear infinite-dimensional evolution equation associated to an analytic semigroup and driven by an irregular noise. As an illustration, we discuss a…

Probability · Mathematics 2010-01-26 Massimiliano Gubinelli , Samy Tindel

The emphasis of this paper is to investigate the high-order approximation of a class of SPDEs with cubic nonlinearity driven by multiplicative noise with the help of the amplitude equations. The highlight of our work is that we improve the…

Probability · Mathematics 2023-08-31 Shiduo Qu , Hongjun Gao

In this work, we will show the existence and uniqueness of the solution to the semi linear stochastic differential equations driven by weighted fractional Brownian motion with delay. We also prove smoothness of the density of the solution…

Probability · Mathematics 2020-12-01 Mahdieh Tahmasebi

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

In this paper, we study the existence of random periodic solutions for semilinear stochastic partial differential equations with multiplicative linear noise on a bounded open domain ${\cal O}\subset {\mathbb R}^d$ with smooth boundary. We…

Probability · Mathematics 2018-03-02 Chunrong Feng , Yue Wu , Huaizhong Zhao

In earlier works on Shape Dynamics (SD), a linear method of solving a particular set of Lichnerowicz-type equations through the implicit function theorem was developed in order to implicitly construct SD's global Hamiltonian and eliminate…

General Relativity and Quantum Cosmology · Physics 2012-01-23 Henrique Gomes

In this paper we consider a general class of second order stochastic partial differential equations on $\mathbb{R}^d$ driven by a Gaussian noise which is white in time and it has a homogeneous spatial covariance. Using the techniques of…

Probability · Mathematics 2014-10-08 Yaozhong Hu , Jingyu Huang , David Nualart , Xiaobin Sun

By using the Malliavin calculus and finite-jump approximations, the Driver-type integration by parts formula is established for the semigroup associated to stochastic differential equations with noises containing a subordinate Brownian…

Probability · Mathematics 2013-08-28 Feng-Yu Wang

We consider the stochastic continuity equation driven by Brownian motion. We use the techniques of the Malliavin calculus to show that the law of the solution has a density with respect to the Lebesgue measure. We also prove that the…

Probability · Mathematics 2018-03-19 David A. C. Mollinedo , Christian Olivera , Ciprian A. Tudor

We consider non-negative solutions to some infinite-dimensional SDEs on $\mathbb{Z}^d$ with H\"older continuous noise coefficients. We prove that if the H\"older exponent is less than $1/2$, solutions are compactly supported for almost all…

Probability · Mathematics 2026-04-01 Thomas Hughes , Marcel Ortgiese

We consider a random variable X satisfying almost-sure conditions involving G:=<DX,-DL^{-1}X> where DX is X's Malliavin derivative and L^{-1} is the inverse Ornstein-Uhlenbeck operator. A lower- (resp. upper-) bound condition on G is proved…

Probability · Mathematics 2009-01-06 Frederi G. Viens

In previous works we have introduced a new method called the lent particle method which is an efficient tool to establish existence of densities for Poisson functionals. We now go further and iterate this method in order to prove smoothness…

Probability · Mathematics 2013-01-29 Nicolas Bouleau , Laurent Denis

We introduce the class of "smooth rough paths" and study their main properties. Working in a smooth setting allows us to discard sewing arguments and focus on algebraic and geometric aspects. Specifically, a Maurer-Cartan perspective is the…

Probability · Mathematics 2024-03-18 Carlo Bellingeri , Peter K. Friz , Sylvie Paycha , Rosa Preiß

Sufficient conditions are given for a hard implicit function theorem to hold. The result is established by an application of the Dynamical Systems Method (DSM). It allows one to solve a class of nonlinear operator equations in the case when…

Functional Analysis · Mathematics 2009-09-23 A. G. Ramm

We provide convergence rates for space approximations of semi-linear stochastic differential equations with multiplicative noise in a Hilbert space. The space approximations we consider are spectral Galerkin and finite elements, and the…

Numerical Analysis · Mathematics 2018-12-19 Sonja Cox , Erika Hausenblas

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 study a class of elliptic SPDEs with additive Gaussian noise on $\mathbb{R}^2 \times M$, with $M$ a $d$-dimensional manifold equipped with a positive Radon measure, and a real-valued non linearity given by the derivative of a smooth…

Probability · Mathematics 2020-08-04 Sergio Albeverio , Francesco C. De Vecchi , Massimiliano Gubinelli

In this paper we show the H\"ormander hypoelliptic theorem for nonlocal operators by a purely probabilistic method: the Malliavin calculus. Roughly speaking, under general H\"ormander's Lie bracket conditions, we show the regularization…

Probability · Mathematics 2019-01-23 Zimo Hao , Xuhui Peng , Xicheng Zhang

We study Neural Optimal Transport in infinite-dimensional Hilbert spaces. In non-regular settings, Semi-dual Neural OT often generates spurious solutions that fail to accurately capture target distributions. We analytically characterize…

Machine Learning · Computer Science 2026-02-17 Jae-Hwan Choi , Jiwoo Yoon , Dohyun Kwon , Jaewoong Choi