Dynamics and large deviations for fractional stochastic partial differential equations with L\'evy noise

TL;DR

This paper investigates fractional stochastic PDEs driven by Le9vy noise, establishing well-posedness, existence of attractors and invariant measures, ergodicity, and large deviation principles, with applications to fractional stochastic Chafee-Infante equations.

Contribution

It provides new results on well-posedness, attractors, invariant measures, ergodicity, and large deviations for fractional SPDEs with Le9vy noise, including specific applications.

Findings

Proved well-posedness via energy estimates.

Established existence of invariant measures and ergodicity.

Derived large deviation principles for solutions.

Abstract

This paper is mainly concerned with a kind of fractional stochastic evolution equations driven by L\'evy noise in a bounded domain. We first state the well-posedness of the problem via iterative approximations and energy estimates. Then, the existence and uniqueness of weak pullback mean random attractors for the equations {are} established by defining a mean random dynamical system. Next, we prove the existence of invariant measures when the problem is autonomous by means of the fact that $H^\gamma(\mathcal{O})$ is compactly embedded in $L^2(\mathcal{O})$ with $\gamma\in (0,1)$. Moreover, the uniqueness of this invariant measure is presented which ensures the ergodicity of the problem. Finally, a large deviation principle result for solutions of SPDEs perturbed by small L\'evy noise and Brownian motion is obtained by a variational formula for positive functionals of a Poisson random measure and Brownian motion. Additionally, the results are illustrated by the fractional stochastic Chafee-Infante equations