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Consider stochastic partial differential equations (SPDEs) with fully local monotone coefficients in a Gelfand triple $V\subseteq H \subseteq V^*$: \begin{align*} \left\{ \begin{aligned} dX(t) & = A(t,X(t))dt + B(t,X(t))dW(t), \quad t\in…

Probability · Mathematics 2025-08-07 Michael Röckner , Shijie Shang , Tusheng Zhang

We study Donsker-Watanabe's delta functions associated with strongly hypoelliptic diffusion processes indexed by a small parameter. They are finite Borel measures on the Wiener space and admit a rough path lift. Our main result is a large…

Probability · Mathematics 2015-01-12 Yuzuru Inahama

In this paper, we provide a criterion on uniform large deviation principles (ULDP) for stochastic differential equations under locally weak monotone conditions and Lyapunov conditions, which can be applied to stochastic systems with…

Probability · Mathematics 2024-09-05 Jian Wang , Hao Yang

In this paper, we consider stochastic reaction-diffusion equations with super-linear drift on the real line $\mathbb{R}$ driven by space-time white noise. A Freidlin-Wentzell large deviation principle is established by a modified weak…

Probability · Mathematics 2025-02-12 Yue Li , Shijie Shang , Jianliang Zhai

This work concerns generalized backward stochastic differential equations, which are coupled with a family of reflecting diffusion processes. First of all, we establish the large deviation principle for forward stochastic differential…

Probability · Mathematics 2024-07-23 Yawen Liu , Huijie Qiao

We consider a reduced order model of an air-breathing hypersonic engine with a time-dependent stochastic inflow that may cause the failure of the engine. The probability of failure is analyzed by the Freidlin-Wentzell theory, the large…

Probability · Mathematics 2012-08-27 George Papanicolaou , Nicholas West , Tzu-Wei Yang

In this paper, we establish the large deviation principles for stochastic porous media equations driven by time-dependent multiplicative noise on $\sigma$-finite measure space $(E,\mathcal{B}(E),\mu)$, and the Laplacian replaced by a…

Probability · Mathematics 2023-04-06 Weina Wu , Jianliang Zhai

This article is devoted to long-time weak approximations of stochastic partial differential equations (SPDEs) evolving in a bounded domain $\mathcal{D} \subset \mathbb{R}^d$, $d \leq 3$, with non-globally Lipschitz and possibly…

Numerical Analysis · Mathematics 2025-07-15 Yingsong Jiang , Xiaojie Wang

In this paper, using Zvonkin type transform, the large deviation principle is proved for stochastic differential equations with Dini continuous drifts, where the existed methods for large deviation principle are unavailable. The method and…

Probability · Mathematics 2018-12-31 Lingyan Cheng , Xing Huang

In this paper, we first provide a criterion on uniform large deviation principles (ULDP) of stochastic differential equations under Lyapunov conditions on the coefficients, which can be applied to stochastic systems with coefficients of…

Probability · Mathematics 2024-02-27 Jifa Jiang , Jian Wang , Jianliang Zhai , Tusheng Zhang

We consider discrete time Markov chains with Polish state space. The large deviations principle for empirical measures of a Markov chain can equivalently be stated in Laplace principle form, which builds on the convex dual pair of relative…

Probability · Mathematics 2026-01-14 Stephan Eckstein

We study a class of reflected McKean-Vlasov diffusions over a convex domain with self-stabilizing coefficients. This includes coefficients that do not satisfy the classical Wasserstein Lipschitz condition. Further, the process is…

Probability · Mathematics 2022-01-19 Daniel Adams , Gonçalo dos Reis , Romain Ravaille , William Salkeld , Julian Tugaut

We prove the the large deviation principle(LDP) for the law of the one-dimensional semilinear stochastic partial differential equations driven by nonlinear multiplicative noise. Firstly, combining the energy estimate and approximation…

Probability · Mathematics 2023-03-09 Qiyong Cao , Hongjun Gao

As an important tool characterizing the long time behavior of Markov processes, the Donsker-Varadhan LDP (large deviation principle) does not directly apply to distribution dependent SDEs/SPDEs since the solutions are non-Markovian. We…

Probability · Mathematics 2020-02-21 Panpan Ren , Feng-Yu Wang

We establish a large deviation principle (LDP) for a class of stochastic porous media equations driven by L\'{e}vy-type noise on a $\sigma$-finite measure space $(E,\mathcal{B}(E),\mu)$, with the Laplacian replaced by a negative definite…

Probability · Mathematics 2023-12-07 Weina Wu , Jianliang Zhai

We consider a stochastic wave equation in spatial dimension three, driven by a Gaussian noise, white in time and with a stationary spatial covariance. The free terms are nonlinear with Lipschitz continuous coefficients. Under suitable…

Probability · Mathematics 2010-01-29 Víctor Ortiz-López , Marta Sanz-Solé

We study large deviations from the invariant measure for nonlinear Schr\"odinger equations with colored noises on determining modes. The proof is based on a new abstract criterion, inspired by [V. Jak\v{s}i\'{c} et al., Comm. Pure Appl.…

Analysis of PDEs · Mathematics 2026-02-03 Yuxuan Chen , Shengquan Xiang

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

In this paper we mainly investigate the strong and weak well-posedness of a class of McKean-Vlasov stochastic (partial) differential equations. The main existence and uniqueness results state that we only need to impose some local…

Probability · Mathematics 2024-01-15 Wei Hong , Shanshan Hu , Wei Liu

In this paper, we consider the large deviations principles (LDPs) for the stochastic linear Schr\"odinger equation and its symplectic discretizations. These numerical discretizations are the spatial semi-discretization based on spectral…

Numerical Analysis · Mathematics 2026-03-06 Chuchu Chen , Jialin Hong , Diancong Jin , Liying Sun