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  Quantifying the parameter dependent basin of the unsafe regime of asymmetric Lévy-noise-induced critical transitions

Ma, J., Xu, Y., Li, Y., Tian, R., Ma, S., Kurths, J. (2021): Quantifying the parameter dependent basin of the unsafe regime of asymmetric Lévy-noise-induced critical transitions. - Applied Mathematics and Mechanics, 42, 1, 65-84.
https://doi.org/10.1007/s10483-021-2672-8

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Ma, Jinzhong1, Autor
Xu, Yong1, Autor
Li, Yongge1, Autor
Tian, Ruilan1, Autor
Ma, Shaojuan1, Autor
Kurths, Jürgen2, Autor              
Affiliations:
1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

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 Zusammenfassung: In real systems, the unpredictable jump changes of the random environment can induce the critical transitions (CTs) between two non-adjacent states, which are more catastrophic. Taking an asymmetric Lévy-noise-induced tri-stable model with desirable, sub-desirable, and undesirable states as a prototype class of real systems, a prediction of the noise-induced CTs from the desirable state directly to the undesirable one is carried out. We first calculate the region that the current state of the given model is absorbed into the undesirable state based on the escape probability, which is named as the absorbed region. Then, a new concept of the parameter dependent basin of the unsafe regime (PDBUR) under the asymmetric Lévy noise is introduced. It is an efficient tool for approximately quantifying the ranges of the parameters, where the noise-induced CTs from the desirable state directly to the undesirable one may occur. More importantly, it may provide theoretical guidance for us to adopt some measures to avert a noise-induced catastrophic CT.

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 Datum: 2020-12-182021-01-15
 Publikationsstatus: Final veröffentlicht
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 Art der Begutachtung: Expertenbegutachtung
 Identifikatoren: DOI: 10.1007/s10483-021-2672-8
MDB-ID: No data to archive
PIKDOMAIN: RD4 - Complexity Science
Research topic keyword: Complex Networks
Research topic keyword: Nonlinear Dynamics
Research topic keyword: Sustainable Development
Model / method: Machine Learning
Organisational keyword: RD4 - Complexity Science
OATYPE: Hybrid Open Access
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Titel: Applied Mathematics and Mechanics
Genre der Quelle: Zeitschrift, SCI, Scopus
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Seiten: - Band / Heft: 42 (1) Artikelnummer: - Start- / Endseite: 65 - 84 Identifikator: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/applied-mathematics-and-mechanics
Publisher: Springer