柳振鑫

个人信息Personal Information

教授

博士生导师

硕士生导师

主要任职:数学科学学院院长、党委副书记

性别:男

毕业院校:吉林大学

学位:博士

所在单位:数学科学学院

学科:应用数学. 应用数学. 概率论与数理统计. 概率论与数理统计

办公地点:数学楼520室

联系方式:0411-84706570,0411-84708351-8520

电子邮箱:zxliu@dlut.edu.cn

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Stochastic stability of measures in gradient systems

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论文类型:期刊论文

发表时间:2016-01-01

发表刊物:PHYSICA D-NONLINEAR PHENOMENA

收录刊物:SCIE、EI

卷号:314

页面范围:9-17

ISSN号:0167-2789

关键字:Fokker-Planck equation; Gradient systems; Gibbs measure; Limit measure; Stochastic stability; White noise perturbation

摘要:Stochastic stability of a compact invariant set of a finite dimensional, dissipative system is studied in our recent work "Concentration and limit behaviors of stationary measures" (Huang et al., 2015) for general white noise perturbations. In particular, it is shown under some Lyapunov conditions that the global attractor of the systems is always stable under general noise perturbations and any strong local attractor in it can be stabilized by a particular family of noise perturbations. Nevertheless, not much is known about the stochastic stability of an invariant measure in such a system. In this paper, we will study the issue of stochastic stability of invariant measures with respect to a finite dimensional, dissipative gradient system with potential function f. As we will show, a special property of such a system is that it is the set of equilibria which is stable under general noise perturbations and the set S-f of global minimal points off which is stable under additive noise perturbations. For stochastic stability of invariant measures in such a system, we will characterize two cases Off, one corresponding to the case of finite Si. and the other one corresponding to the case when S-f is of positive Lebesgue measure, such that either some combined Dirac measures or the normalized Lebesgue measure on S-f is stable under additive noise perturbations. However, we will show by constructing an example that such measure stability can fail even in the simplest situation, i.e., in 1-dimension there exists a potential function f such that S-f consists of merely two points but no invariant measure of the corresponding gradient system is stable under additive noise perturbations. Crucial roles played by multiplicative and additive noise perturbations to the measure stability of a gradient system will also be discussed. In particular, the nature of instabilities of the normalized Lebesgue measure on S-f under multiplicative noise perturbations will be exhibited by an example. (C) 2015 Elsevier B.V. All rights reserved.