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  Direction-dependent noise-induced synchronization in mobile oscillators

Shajan, E., Ghosh, D., Kurths, J., Shrimali, M. D. (2023): Direction-dependent noise-induced synchronization in mobile oscillators. - Chaos, 33, 053108.
https://doi.org/10.1063/5.0146983

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 Creators:
Shajan, Emilda 1, Author
Ghosh, Dibakar 1, Author
Kurths, Jürgen2, Author              
Shrimali, Manish Dev 2, Author
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1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

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 Abstract: Synchronization among uncoupled oscillators can emerge when common noise is applied on them and is famously known as noise-induced synchronization. In previous studies, it was assumed that common noise may drive all the oscillators at the same time when they are static in space. Understanding how to develop a mathematical model that apply common noise to only a fraction of oscillators is of significant importance for noise-induced synchronization. Here, we propose a direction-dependent noise field model for noise-induced synchronization of an ensemble of mobile oscillators/agents, and the effective noise on each moving agent is a function of its direction of motion. This enables the application of common noise if the agents are oriented in the same direction. We observe not only complete synchronization of all the oscillators but also clustered states as a function of the ensemble density beyond a critical value of noise intensity, which is a characteristic of the internal dynamics of the agents. Our results provide a deeper understanding on noise-induced synchronization even in mobile agents and how the mobility of agents affects the synchronization behaviors.

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Language(s): eng - English
 Dates: 2023-05-092023-05-09
 Publication Status: Finally published
 Pages: 8
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1063/5.0146983
MDB-ID: No data to archive
PIKDOMAIN: RD4 - Complexity Science
PIKDOMAIN: RD4 - Complexity Science
Research topic keyword: Complex Networks
Research topic keyword: Nonlinear Dynamics
Model / method: Machine Learning
 Degree: -

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Title: Chaos
Source Genre: Journal, SCI, Scopus, p3
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Pages: - Volume / Issue: 33 Sequence Number: 053108 Start / End Page: - Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/180808
Publisher: American Institute of Physics (AIP)