Stochastic stability of measures in gradient systems

被引:10
|
作者
Huang, Wen [1 ,2 ]
Ji, Min [3 ]
Liu, Zhenxin [4 ]
Yi, Yingfei [5 ,6 ,7 ]
机构
[1] Sichuan Univ, Sch Math, Chengdu 610064, Sichuan, Peoples R China
[2] Univ Sci & Technol China, Sch Math Sci, Hefei 230026, Anhui, Peoples R China
[3] Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100080, Peoples R China
[4] Dalian Univ Technol, Sch Math Sci, Dalian 116024, Peoples R China
[5] Univ Alberta, Dept Math & Stat Sci, Edmonton, AB T6G 2G1, Canada
[6] Jilin Univ, Sch Math, Changchun 130012, Peoples R China
[7] Georgia Inst Technol, Sch Math, Atlanta, GA 30332 USA
基金
加拿大自然科学与工程研究理事会;
关键词
Fokker-Planck equation; Gradient systems; Gibbs measure; Limit measure; Stochastic stability; White noise perturbation; NOISE;
D O I
10.1016/j.physd.2015.09.014
中图分类号
O29 [应用数学];
学科分类号
070104 ;
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.
引用
收藏
页码:9 / 17
页数:9
相关论文
共 50 条
  • [1] ASYMPTOTIC STABILITY OF EVOLUTION SYSTEMS OF PROBABILITY MEASURES FOR NONAUTONOMOUS STOCHASTIC SYSTEMS: THEORETICAL RESULTS AND APPLICATIONS
    Wang, Renhai
    Caraballo, Tomas
    Tuan, Nguyen Huy
    PROCEEDINGS OF THE AMERICAN MATHEMATICAL SOCIETY, 2023, 151 (06) : 2449 - 2458
  • [2] On stability of stochastic switched systems
    Chatterjee, D
    Liberzon, D
    2004 43RD IEEE CONFERENCE ON DECISION AND CONTROL (CDC), VOLS 1-5, 2004, : 4125 - 4127
  • [3] Stability of stochastic systems with jumps
    Boukas, EK
    Yang, H
    MATHEMATICAL PROBLEMS IN ENGINEERING, 1996, 3 (02) : 173 - 185
  • [4] Stochastic Stability of Dynamical Systems Driven by Levy Processes
    Tamba, T. A.
    Turnip, A.
    2015 15TH INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION AND SYSTEMS (ICCAS), 2015, : 1123 - 1127
  • [5] Numerical Simulation of Dynamic Stability of Fractional Stochastic Systems
    Deng, Jian
    INTERNATIONAL JOURNAL OF STRUCTURAL STABILITY AND DYNAMICS, 2018, 18 (10)
  • [6] Stability of Stochastic Gradient Descent on Nonsmooth Convex Losses
    Bassily, Raef
    Feldman, Vitaly
    Guzman, Cristobal
    Talwar, Kunal
    ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 33, NEURIPS 2020, 2020, 33
  • [7] Stability in terms of two measures for stochastic differential equations
    Yuan, CG
    DYNAMICS OF CONTINUOUS DISCRETE AND IMPULSIVE SYSTEMS-SERIES A-MATHEMATICAL ANALYSIS, 2003, 10 (06): : 895 - 910
  • [8] INVARIANT MEASURES OF STOCHASTIC DELAY LATTICE SYSTEMS
    Chen, Zhang
    Li, Xiliang
    Wang, Bixiang
    DISCRETE AND CONTINUOUS DYNAMICAL SYSTEMS-SERIES B, 2021, 26 (06): : 3235 - 3269
  • [9] Periodic measures of stochastic delay lattice systems
    Li, Dingshi
    Wang, Bixiang
    Wang, Xiaohu
    JOURNAL OF DIFFERENTIAL EQUATIONS, 2021, 272 : 74 - 104
  • [10] Asymptotic stability of evolution systems of probability measures of stochastic discrete modified Swift-Hohenberg equations
    Wang, Fengling
    Caraballo, Tomas
    Li, Yangrong
    Wang, Renhai
    STOCHASTICS AND PARTIAL DIFFERENTIAL EQUATIONS-ANALYSIS AND COMPUTATIONS, 2024, 12 (02): : 1374 - 1415