Large Deviations for Locally Monotone Stochastic Partial Differential Equations Driven by Lévy Noise

Weina Wu (), Jianliang Zhai () and Jiahui Zhu ()
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Weina Wu: Nanjing University of Finance and Economics
Jianliang Zhai: University of Science and Technology of China
Jiahui Zhu: Zhejiang University of Technology

Journal of Theoretical Probability, 2025, vol. 38, issue 4, 1-33

Abstract: Abstract We establish a Freidlin–Wentzell type large deviation principle (LDP) for a class of SPDEs with locally monotone coefficients driven by Lévy noise. Our results improve the work on this topic (Bernoulli, 2018), because we drop the compactness embedding assumptions, and we also make the conditions for the coefficient of the noise term more specific and weaker. We utilize an improved sufficient criterion of Budhiraja, Chen, Dupuis, and Maroulas for functions of Poisson random measures. To remove the compactness embedding assumptions, we adopt a technical procedure introduced in SIAM J. Math. Anal., 2024, which includes the methods of time discretization, a cutoff argument, and relative entropy estimates of a sequence of probability measures. As an application, we derive, for the first time, the Freidlin–Wentzell-type LDPs for SPDEs driven by Lèvy noise in unbounded domains of $$\mathbb {R}^d$$ R d , which are generally lack of compactness embedding properties. Such examples include the stochastic p-Laplace equation, stochastic Burgers-type equations, stochastic 2D Navier–Stokes equations, and stochastic equations of non-Newtonian fluids, among others

Keywords: Freidlin–Wentzell type large deviation principle; Lévy noise; locally monotone; stochastic partial differential equations; 35Q35; 35R60; 60H15; 60G51; 60F10 (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10959-025-01437-6

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