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dc.contributor.authorAshwin, P
dc.contributor.authorNewman, J
dc.date.accessioned2021-04-19T08:01:36Z
dc.date.issued2021-04-26
dc.description.abstractPhysical measures are invariant measures that characterise “typical” behaviour of trajectories started in the basin of chaotic attractors for autonomous dynamical systems. In this paper, we make some steps towards extending this notion to more general nonautonomous (time-dependent) dynamical systems. There are barriers to doing this in general in a physically meaningful way, but for systems that have autonomous limits, one can define a physical measure in relation to the physical measure in the past limit. We use this to understand cases where rate-dependent tipping between chaotic attractors can be quantified in terms of “tipping probabilities”. We demonstrate this for two examples of perturbed systems with multiple attractors undergoing a parameter shift. The first is a double-scroll system of Chua et al., and the second is a Stommel model forced by Lorenz chaos.en_GB
dc.description.sponsorshipEuropean Commissionen_GB
dc.identifier.citationPublished online 26 April 2021en_GB
dc.identifier.doi10.1140/epjs/s11734-021-00114-z
dc.identifier.grantnumber820970en_GB
dc.identifier.urihttp://hdl.handle.net/10871/125389
dc.language.isoenen_GB
dc.publisherEDP Sciences / Springer Verlag / Società Italiana di Fisicaen_GB
dc.relation.urlhttps://github.com/peterashwin/ashwin-newman-2021en_GB
dc.rights© The Author(s) 2021. 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/.
dc.titlePhysical invariant measures and tipping probabilities for chaotic attractors of asymptotically autonomous systemsen_GB
dc.typeArticleen_GB
dc.date.available2021-04-19T08:01:36Z
dc.identifier.issn1951-6355
dc.descriptionThis is the final version. Available on open access from EDP Sciences via the DOI in this recorden_GB
dc.descriptionMatlab code for a selection of the simulations and an animation of Fig. 1 are available from: https://github.com/peterashwin/ashwin-newman-2021.en_GB
dc.identifier.journalEuropean Physical Journal Special Topicsen_GB
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_GB
dcterms.dateAccepted2021-04-14
exeter.funder::European Commissionen_GB
rioxxterms.versionVoRen_GB
rioxxterms.licenseref.startdate2021-04-14
rioxxterms.typeJournal Article/Reviewen_GB
refterms.dateFCD2021-04-18T10:48:14Z
refterms.versionFCDAM
refterms.dateFOA2021-04-28T15:12:14Z
refterms.panelBen_GB


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© The Author(s) 2021. 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/.
Except where otherwise noted, this item's licence is described as © The Author(s) 2021. 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/.