Banach空间中随机延迟抛物方程随机吸引子的Hausdorff维数

IF 1.7 2区 数学 Q2 MATHEMATICS, APPLIED
Wenjie Hu, Tomás Caraballo, Yueliang Duan
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引用次数: 0

摘要

本文的主要目的是给出Banach空间中一类随机延迟抛物方程的随机吸引子的Hausdorff维数的上界。结合Banach空间有限子空间的压缩性质和覆盖引理,得到了随机吸引子维数的估计,推广了Hilbert空间中建立的方法。由于缺乏光滑的内积几何结构,我们采用了基于线性确定性部分的指数二分法的相空间状态分解来代替随机偏微分方程中有限秩的正交投影。所得到的随机吸引子维数仅取决于所研究方程的内部特征,如线性部分的谱和非线性项的随机Lipschitz常数,而不像现有的工作那样与相空间紧嵌入到另一个Banach空间有关。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Hausdorff Dimension of Random Attractors for a Stochastic Delayed Parabolic Equation in Banach Spaces

The main purpose of this paper is to give an upper bound of Hausdorff dimension of random attractors for a stochastic delayed parabolic equation in Banach spaces. The estimation of dimensions of random attractors are obtained by combining the squeezing property and a covering lemma of finite subspace of Banach spaces, which generalizes the method established in Hilbert spaces. Due to the lack of smooth inner product geometry structure, we adopt the state decomposition of phase space based on the exponential dichotomy of the linear deterministic part of the studied equations instead of orthogonal projectors with finite ranks used for stochastic partial differential equations. The obtained dimension of the random attractors depends only on the inner characteristics of the studied equation, such as spectrum of the linear part and the random Lipschitz constant of the nonlinear term, while not relating to the compact embedding of the phase space to another Banach space as the existing works did.

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来源期刊
CiteScore
3.30
自引率
5.60%
发文量
103
审稿时长
>12 weeks
期刊介绍: The Applied Mathematics and Optimization Journal covers a broad range of mathematical methods in particular those that bridge with optimization and have some connection with applications. Core topics include calculus of variations, partial differential equations, stochastic control, optimization of deterministic or stochastic systems in discrete or continuous time, homogenization, control theory, mean field games, dynamic games and optimal transport. Algorithmic, data analytic, machine learning and numerical methods which support the modeling and analysis of optimization problems are encouraged. Of great interest are papers which show some novel idea in either the theory or model which include some connection with potential applications in science and engineering.
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