Large deviation principles of 2D stochastic Navier–Stokes equations with Lévy noises

Taking the consideration of two-dimensional stochastic Navier–Stokes equations with multiplicative Lévy noises, where the noises intensities are related to the viscosity, a large deviation principle is established by using the weak convergence method skillfully, when the viscosity converges to 0. Due to the appearance of the jumps, it is difficult to close the energy estimates and obtain the desired convergence. Hence, one cannot simply use the weak convergence approach. To overcome the difficulty, one introduces special norms for new arguments and more careful analysis.

The Kramers problem for SDEs driven by small, accelerated Lévy noise with exponentially light jumps

We establish Freidlin–Wentzell results for a nonlinear ordinary differential equation starting close to the stable state [Formula: see text], say, subject to a perturbation by a stochastic integral which is driven by an [Formula: see text]-small and [Formula: see text]-accelerated Lévy process with exponentially light jumps. For this purpose, we derive a large deviations principle for the stochastically perturbed system using the weak convergence approach developed by Budhiraja, Dupuis, Maroulas and collaborators in recent years. In the sequel, we solve the associated asymptotic first escape problem from the bounded neighborhood of [Formula: see text] in the limit as [Formula: see text] which is also known as the Kramers problem in the literature.

Large deviations for a stochastic Cahn–Hilliard equation in Hölder norm

We consider a stochastic Cahn–Hilliard equation driven by a space–time white noise. We prove that the law of the solution satisfies a large deviations principle in the Hölder norm. Our proof is based on the weak convergence approach for large deviations.

Central limit theorem and moderate deviations for a perturbed stochastic Cahn–Hilliard equation

In this paper, we prove a central limit theorem and a moderate deviation principle for a perturbed stochastic Cahn–Hilliard equation defined on [Formula: see text] with [Formula: see text]. This equation is driven by a space-time white noise. The weak convergence approach plays an important role.

Moderate deviation principle for multivalued stochastic differential equations

In this paper, we prove a moderate deviation principle for the multivalued stochastic differential equations whose proof are based on recently well-developed weak convergence approach. As an application, we obtain the moderate deviation principle for reflected Brownian motion.

Large deviations for stochastic integrodifferential equations of the Itô type with multiple randomness

A Freidlin-Wentzell type large deviation principle is derived for a class of It? type stochastic integrodifferential equations driven by a finite number of multiplicative noises of the Gaussian type. The weak convergence approach is used here to prove the Laplace principle, equivalently large deviation principle.

RARE EVENTS IN THE STOCHASTIC CAMASSA–HOLM EQUATION

We investigate rare or small probability events in the context of large deviations of the stochastic Camassa–Holm equation. By the weak convergence approach and regularization, we get large deviations of the regularized equation. Then, by stochastic equations exponentially equivalent to the corresponding laws, we get large deviations of the stochastic Camassa–Holm equation.

Large deviations for multidimensional state-dependent shot-noise processes

Shot-noise processes are used in applied probability to model a variety of physical systems in, for example, teletraffic theory, insurance and risk theory, and in the engineering sciences. In this paper we prove a large deviation principle for the sample-paths of a general class of multidimensional state-dependent Poisson shot-noise processes. The result covers previously known large deviation results for one-dimensional state-independent shot-noise processes with light tails. We use the weak convergence approach to large deviations, which reduces the proof to establishing the appropriate convergence of certain controlled versions of the original processes together with relevant results on existence and uniqueness.