Measure and statistical attractors for nonautonomous dynamical systems
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Published version
Author(s)
Oljača, Lea
Ashwin, Peter
Rasmussen, Martin
Type
Journal Article
Abstract
Various inequivalent notions of attraction for autonomous dynamical systems have been proposed, each of them useful to understand specific aspects of attraction. Milnor’s notion of a measure attractor considers invariant sets with positive measure basin of attraction, while Ilyashenko’s weaker notion of a statistical attractor considers positive measure points that approach the invariant set in terms of averages. In this paper we propose generalisations of these notions to nonautonomous evolution processes in continuous time. We demonstrate that pullback/forward measure/statistical attractors can be defined in an analogous manner and relate these to the respective autonomous notions when an autonomous system is considered as nonautonomous. There are some subtleties even in this special case–we illustrate an example of a two-dimensional flow with a one-dimensional measure attractor containing a single point statistical attractor. We show that the single point can be a pullback measure attractor for this system. Finally, for the particular case of an asymptotically autonomous system (where there are autonomous future and past limit systems) we relate pullback (respectively, forward) attractors to the past (respectively, future) limit systems.
Date Issued
2024-09-01
Date Acceptance
2022-07-22
Citation
Journal of Dynamics and Differential Equations, 2024, 36, pp.2375-2411
ISSN
1040-7294
Publisher
Springer Science and Business Media LLC
Start Page
2375
End Page
2411
Journal / Book Title
Journal of Dynamics and Differential Equations
Volume
36
Copyright Statement
© The Author(s) 2022, corrected publication 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
License URL
Identifier
https://link.springer.com/article/10.1007/s10884-022-10196-5
Publication Status
Published
Date Publish Online
2022-09-07