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Uniform Large Deviation Principles of Fractional Reaction-Diffusion Equations Driven by Superlinear Multiplicative Noise on R^n
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In this paper, we investigate the uniform large deviation principle of the fractional stochastic reaction-diffusion equation on the entire space R^n as the noise intensity approaches zero. The nonlinear drift term is dissipative and has a polynomial growth of any order. The nonlinear diffusion term is locally Lipschitz continuous and has a superlinear growth rate. By the weak convergence method, we establish the Freidlin-Wentzell uniform large deviations over bounded initial data as well as the Dembo-Zeitouni uniform large deviations over compact initial data. The main difficulties are caused by the superlinear growth of noise coefficients and the non-compactness of Sobolev embeddings on unbounded domains. The dissipativeness of the drift term and the idea of uniform tail-ends estimates of solutions are employed to circumvent these difficulties.
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Uniform Large Deviations of Mckean-Vlasov Stochastic Fractional $(\alpha,p)$-Laplacian Equations Driven by Superlinear Noise on $\mathbb{R}^d$
McKean–Vlasov stochastic fractional (α,p)-Laplacian equations with superlinear multiplicative noise on R^d are globally well-posed and satisfy Freidlin–Wentzell and Dembo–Zeitouni uniform large deviation principles.
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