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In this paper, we establish a large deviation principle for a type of stochastic partial differential equations (SPDEs) with locally monotone coefficients driven by L\'evy noise. The weak convergence method plays an important role.

Probability · Mathematics 2016-06-08 Jie Xiong , Jianliang Zhai

We prove the small-noise large deviation principle for the three-dimensional primitive equations with transport noise and turbulent pressure. Transport noise is important for geophysical fluid dynamics applications, as it takes into account…

Probability · Mathematics 2025-12-23 Antonio Agresti , Esmée Theewis

We study the asymptotic behaviour of solutions of Forward Backward Stochastic Differential Equations in the coupled case, when the diffusion coefficient of the forward equation is multiplicatively perturbed by a small parameter that…

Probability · Mathematics 2013-02-27 Ana Bela Cruzeiro , André de Oliveira Gomes

In this article we prove the existence and uniqueness for degenerate stochastic differential equations with Sobolev (possibly singular) drift and diffusion coefficients in a generalized sense. In particular, our result covers the classical…

Probability · Mathematics 2010-09-07 Xicheng Zhang

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

Let $L$ be a positive definite self-adjoint operator on the $L^2$-space associated to a $\si$-finite measure space. Let $H$ be the dual space of the domain of $L^{1/2}$ w.r.t. $L^2(\mu)$. By using an It\^o type inequality for the $H$-norm…

Probability · Mathematics 2014-02-26 Michael Rockner , Feng-Yu Wang

In this paper, we establish large deviation principle for the strong solution of evolutionary p-Laplace equation driven by small multiplicative Brownian noise, where the weak convergence approach plays a key role. Moreover, by using…

Probability · Mathematics 2022-10-21 Kavin R , Ananta K Majee

We investigate the quality of space approximation of a class of stochastic integral equations of convolution type with Gaussian noise. Such equations arise, for example, when considering mild solutions of stochastic fractional order partial…

Numerical Analysis · Mathematics 2022-01-05 Erika Hausenblas , Mihály Kovács

The stochastic Landau-Lifshitz-Bloch equation in dimensions 1; 2; and 3 perturbed by pure jump noise is considered in the Marcus canonical form. A proof for existence of a martingale solution is given. The proof uses the Faedo-Galerkin…

Probability · Mathematics 2023-02-13 Soham Gokhale , Utpal Manna

This paper focuses on systems of nonlinear second-order stochastic differential equations with multi-scales. The motivation for our study stems from mathematical physics and statistical mechanics, for examples, Langevin dynamics and…

Probability · Mathematics 2024-04-08 Nhu N. Nguyen , George Yin

This paper is devoted to the study of generalised time-fractional evolution equations involving Caputo type derivatives. Using analytical methods and probabilistic arguments we obtain well-posedness results and stochastic representations…

Analysis of PDEs · Mathematics 2022-05-03 M. E. Hernández-Hernández , V. N. Kolokoltsov , L. Toniazzi

We study the asymptotics of a two-dimensional stochastic differential system with a degenerate diffusion matrix. This system describes the dynamics of a population where individuals contribute to the degradation of their environment through…

Dynamical Systems · Mathematics 2024-05-06 Pierre Collet , Claire Ecotière , Sylvie Méléard

We prove that the stationary measure associated to a boundary driven exclusion process in any dimension satisfies a large deviation principle with rate function given by the quasi potential of the Freidlin and Wentzell theory.

Probability · Mathematics 2009-08-14 Jonathan Farfan

We review here the development of the general formalism for the study of fermion propagation in the presence of stochastic media. This formalism allows the systematic derivation of evolution equations for averaged quantities as survival…

High Energy Physics - Phenomenology · Physics 2007-05-23 E. Torrente-Lujan

In this paper, a probabilistic interpretation for the viscosity solution of a parabolic partial differential equation is obtained by virtue of the solution of a class of quadratic backward stochastic differential equations (BSDEs, for…

Probability · Mathematics 2022-09-21 Yufeng Shi , Jiaqiang Wen , Zhi Yang

In this work we prove an analogue, for partial differential equations on the space of probability measures, of the classical vanishing viscosity result known for equations on the Euclidean space. Our result allows in particular to show that…

Probability · Mathematics 2022-06-07 Ludovic Tangpi

The theory of stochastic approximations form the theoretical foundation for studying convergence properties of many popular recursive learning algorithms in statistics, machine learning and statistical physics. Large deviations for…

Probability · Mathematics 2025-02-05 Henrik Hult , Adam Lindhe , Pierre Nyquist , Guo-Jhen Wu

In this article we present a way of treating stochastic partial differential equations with multiplicative noise by rewriting them as stochastically perturbed evolutionary equations in the sense of \cite{picardbook}, where a general…

Probability · Mathematics 2016-11-08 André Süß , Marcus Waurick

Unique existence of analytically strong solutions to stochastic partial differential equations (SPDE) with drift given by the subdifferential of a quasi-convex function and with general multiplicative noise is proven. The proof applies a…

Probability · Mathematics 2011-04-22 Benjamin Gess

Consider stochastic partial differential equations (SPDEs) with fully local monotone coefficients in a Gelfand triple $V\subseteq H\subseteq V^*$ $$ \left\{ \begin{align} &dX_t=A(t,X_t)dt+B(t,X_t)dW_t,\ t\in (0,T]\\\\& X_0=x\in H,…

Probability · Mathematics 2024-01-11 Tianyi Pan , Shijie Shang , Jianliang Zhai , Tusheng Zhang
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