乘法噪声驱动的随机广义伯格斯-赫胥黎方程的均匀大偏差

Pub Date : 2024-06-01 DOI:10.1007/s10255-024-1129-0

Hui Guo, Xi-liang Li, Jin Ma

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引用次数: 0

摘要

本文针对乘性小噪声驱动的随机一维广义伯格斯-赫胥黎方程，在无限维巴纳赫空间的有界子集（不一定属于紧凑集）中，建立了均匀地关于初始条件的弗雷德林-温采尔型大偏差原理。证明基于[Budhiraja、Dupuis 和 Salins；Trans. Amer. Math. Soc., 2019]获得的弱收敛方法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Uniform Large Deviations for Stochastic Generalized Burgers-Huxley Equation Driven by Multiplicative Noise

In this paper, we establish a Freidlin-Wentzell type large deviation principle uniformly with respect to initial condition in bounded subsets, that do not necessarily belongs to compact sets, of an infinite dimensional Banach space for stochastic 1D generalized Burgers-Huxley equation driven by multiplicative small noise. The proof is based on the weak convergence approach obtained by [Budhiraja, Dupuis and Salins; Trans. Amer. Math. Soc., 2019].

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