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  Canard Cascading in Networks with Adaptive Mean-Field Coupling

Balzer, J., Berner, R., Lüdge, K., Wieczorek, S., Kurths, J., Yanchuk, S. (2024): Canard Cascading in Networks with Adaptive Mean-Field Coupling. - Physical Review Letters, 133, 237401.
https://doi.org/10.1103/PhysRevLett.133.237401

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 Creators:
Balzer, J.1, Author
Berner, R.1, Author
Lüdge, K.1, Author
Wieczorek, S.1, Author
Kurths, Jürgen2, Author              
Yanchuk, Serhiy2, Author              
Affiliations:
1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

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 Abstract: Canard cascading (CC) is observed in dynamical networks with global adaptive coupling. It is a slow-fast phenomenon characterized by a recurrent sequence of fast transitions between distinct and slowly evolving quasistationary states. In this Letter, we uncover the dynamical mechanisms behind CC, using an illustrative example of globally and adaptively coupled semiconductor lasers, where CC represents sequential switching on and off the lasers. First, we show that CC is a robust and truly adaptive network effect that is scalable with network size and does not occur without adaptation. Second, we uncover multiple saddle slow manifolds (unstable quasistationary states) linked by heteroclinic orbits (fast transitions) in the phase space of the system. This allows us to identify CC with a novel heteroclinic canard orbit that organizes different unstable quasistationary states into an intricate slow-fast limit cycle. Although individual quasistationary states are unstable (saddles), the CC cycle as a whole is attractive and robust to parameter changes.

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Language(s): eng - English
 Dates: 2024-12-022024-12-02
 Publication Status: Finally published
 Pages: 7
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1103/PhysRevLett.133.237401
MDB-ID: No data to archive
PIKDOMAIN: RD4 - Complexity Science
Organisational keyword: RD4 - Complexity Science
Research topic keyword: Nonlinear Dynamics
Research topic keyword: Complex Networks
Model / method: Machine Learning
 Degree: -

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Title: Physical Review Letters
Source Genre: Journal, SCI, Scopus, p3
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Pages: - Volume / Issue: 133 Sequence Number: 237401 Start / End Page: - Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/journals391
Publisher: American Physical Society (APS)