On limit measures and their supports for stochastic ordinary differential equations

被引:0
|
作者
Xu, Tianyuan [1 ]
Chen, Lifeng [1 ]
Jiang, Jifa [1 ]
机构
[1] Shanghai Normal Univ, Math & Sci Coll, Shanghai 200234, Peoples R China
基金
中国国家自然科学基金;
关键词
Large deviations; Stationary measure; Limit measure; Concentration; Axiom A system; Poincare-Bendixson property; STRUCTURAL STABILITY; DYNAMICAL-SYSTEMS; LARGE DEVIATIONS; MONOTONE; THEOREM;
D O I
10.1016/j.jde.2023.04.005
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
摘要
This paper studies limit measures of stationary measures of stochastic ordinary differential equations on the Euclidean space and tries to determine which invariant measures of an unperturbed system will sur-vive. Under the assumption for SODEs to admit the Freidlin-Wentzell or Dembo-Zeitouni large deviations principle with weaker compactness condition, we prove that limit measures are concentrated away from repellers which are topologically transitive, or equivalent classes, or admit Lebesgue measure zero. We also preclude concentrations of limit measures on acyclic saddle or trap chains. This illustrates that limit measures are concentrated on Liapunov stable compact invariant sets. Applications are made to the Morse- Smale systems, the Axiom A systems including structural stability systems and separated star systems, the gradient or gradient-like systems, those systems possessing the Poincare-Bendixson property with a finite number of limit sets to obtain that limit measures live on Liapunov stable critical elements, Liapunov stable basic sets, Liapunov stable equilibria and Liapunov stable limit sets including equilibria, limit cycles and saddle or trap cycles, respectively. Four nontrivial examples admitting a unique limit measure are provided, two of which possess infinite equivalent classes and their supports are Liapunov stable periodic orbits, one is supported on saddle-nodes.(c) 2023 Elsevier Inc. All rights reserved.
引用
收藏
页码:72 / 99
页数:28
相关论文
共 50 条
  • [21] MODERATE DEVIATION PRINCIPLES FOR STOCHASTIC DIFFERENTIAL EQUATIONS WITH JUMPS
    Budhiraja, Amarjit
    Dupuis, Paul
    Ganguly, Arnab
    ANNALS OF PROBABILITY, 2016, 44 (03) : 1723 - 1775
  • [22] Asymptotic behavior for delayed backward stochastic differential equations
    Manga, Clement
    Aman, Auguste
    Tuo, Navegue
    COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION, 2023,
  • [23] W-symmetries of Ito stochastic differential equations
    Gaeta, G.
    JOURNAL OF MATHEMATICAL PHYSICS, 2019, 60 (05)
  • [24] Analysis of stochastic neutral fractional functional differential equations
    Alagesan Siva Ranjani
    Murugan Suvinthra
    Krishnan Balachandran
    Yong-Ki Ma
    Boundary Value Problems, 2022
  • [25] LARGE DEVIATIONS FOR NEUTRAL STOCHASTIC FUNCTIONAL DIFFERENTIAL EQUATIONS
    Suo, Yongqiang
    Yuan, Chenggui
    COMMUNICATIONS ON PURE AND APPLIED ANALYSIS, 2020, 19 (04) : 2369 - 2384
  • [26] LARGE DEVIATIONS FOR STOCHASTIC DIFFERENTIAL EQUATIONS DRIVEN BY SEMIMARTINGALES
    Huang, Q.
    Wei, W.
    Duan, J.
    THEORY OF PROBABILITY AND ITS APPLICATIONS, 2024, 69 (03) : 460 - 487
  • [27] Invariant Manifolds for Random and Stochastic Partial Differential Equations
    Caraballo, Tomas
    Duan, Jinqiao
    Lu, Kening
    Schmalfuss, Bjoern
    ADVANCED NONLINEAR STUDIES, 2010, 10 (01) : 23 - 52
  • [28] DIFFUSION APPROXIMATION FOR FULLY COUPLED STOCHASTIC DIFFERENTIAL EQUATIONS
    Roeckner, Michael
    Xie, Longjie
    ANNALS OF PROBABILITY, 2021, 49 (03) : 1205 - 1236
  • [29] Analysis of stochastic neutral fractional functional differential equations
    Siva Ranjani, Alagesan
    Suvinthra, Murugan
    Balachandran, Krishnan
    Ma, Yong-Ki
    BOUNDARY VALUE PROBLEMS, 2022, 2022 (01)
  • [30] Partial differential equations and stochastic methods in molecular dynamics
    Lelievre, Tony
    Stoltz, Gabriel
    ACTA NUMERICA, 2016, 25 : 681 - 880