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  Identification of single- and double-well coherence–incoherence patterns by the binary distance matrix

dos Santos, V., Sales, M. R., Muni, S. S., Szezech, J. D., Batista, A. M., Yanchuk, S., Kurths, J. (2023): Identification of single- and double-well coherence–incoherence patterns by the binary distance matrix. - Communications in Nonlinear Science and Numerical Simulation, 125, 107390.
https://doi.org/10.1016/j.cnsns.2023.107390

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 Creators:
dos Santos, Vagner1, Author
Sales, Matheus Rolim1, Author
Muni, Sishu Shankar1, Author
Szezech, José Danilo1, Author
Batista, Antonio Marcos1, Author
Yanchuk, Serhiy2, Author              
Kurths, Jürgen2, Author              
Affiliations:
1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

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 Abstract: The study of chimera states or, more generally, coherence–incoherence patterns has led to the development of several tools for their identification and characterization. In this work, we extend the eigenvalue decomposition method to distinguish between single-well (SW) and double-well (DW) patterns. By applying our method, we are able to identify the following four types of dynamical patterns in a ring of nonlocally coupled Chua circuits and nonlocally coupled cubic maps: SW cluster, SW coherence–incoherence pattern, DW cluster, and DW coherence–incoherence. In a ring-star network of Chua circuits, we investigate the influence of adding a central node on the spatio-temporal patterns. Our results show that increasing the coupling with the central node favors the occurrence of SW coherence–incoherence states. We observe that the boundaries of the attraction basins resemble fractal and riddled structures.

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Language(s): eng - English
 Dates: 2023-07-072023-10
 Publication Status: Finally published
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1016/j.cnsns.2023.107390
MDB-ID: No data to archive
PIKDOMAIN: RD4 - Complexity Science
Organisational keyword: RD4 - Complexity Science
Research topic keyword: Complex Networks
Research topic keyword: Nonlinear Dynamics
Model / method: Open Source Software
Working Group: Network- and machine-learning-based prediction of extreme events
 Degree: -

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Title: Communications in Nonlinear Science and Numerical Simulation
Source Genre: Journal, SCI, Scopus, p3
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Pages: - Volume / Issue: 125 Sequence Number: 107390 Start / End Page: - Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/201610061
Publisher: Elsevier