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Hypercontractivity for functional stochastic partial differential equations

机译:泛函随机偏微分方程的超收缩性

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Explicit sufficient conditions on the hypercontractivity are presented for two classes of functional stochastic partial differential equationsdriven by, respectively, non-degenerate and degenerate Gaussian noises. Consequently, these conditions imply that the associated Markov semigroup is $L^2$-compact and exponentially convergent to the stationary distribution in entropy, variance and total variational norm.?As the log-Sobolev inequality is invalid under the framework, we apply a criterion presented in a recent paper using Harnack inequality, coupling property and Gaussian concentration property of the stationary distribution. To verify the concentration property, we prove a Fernique type inequality for infinite-dimensional Gaussian processes which might be interesting by itself.
机译:给出了分别由非简并和简并的高斯噪声驱动的两类函数随机偏微分方程的超收缩性的充分充分条件。因此,这些条件意味着相关的马尔可夫半群是-L ^ 2 $-紧实的,并且在熵,方差和总变分范数上呈指数收敛于平稳分布。在最近的论文中提出了使用Harnack不等式,耦合特性和高斯集中特性的平稳分布标准。为了验证浓度特性,我们证明了无限维高斯过程的Fernique型不等式,这本身可能很有趣。

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