Investigation of transient extreme events in a mutually coupled star network of theoretical Brusselator system

In this article, we present evidence of a distinct class of extreme events that occur during the transient chaotic state within network modeling using the Brusselator with a mutually coupled star network. We analyze the phenomenon of transient extreme events in the network by focusing on the lifetimes of chaotic states. These events are identified through the finite-time Lyapunov exponent and quantified using threshold and statistical methods, including the probability distribution function (PDF), generalized extreme value (GEV) distribution, and return period plots. We also evaluate the transitions of these extreme events by examining the average synchronization error and the system's energy function. Our findings, validated across networks of various sizes, demonstrate consistent patterns and behaviors, contributing to a deeper understanding of transient extreme events in complex networks.

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Publication Details

Journal
Chaos An Interdisciplinary Journal of Nonlinear Science
Published
2024-09-01
DOI
https://doi.org/10.1063/5.0232021
Citations
1
Primary Topic
Nonlinear Dynamics and Pattern Formation
Type
article
Field-Weighted Citation Impact
0.35
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article

Investigation of transient extreme events in a mutually coupled star network of theoretical Brusselator system

I. Manimehan, K. Thamilmaran, S. V. Manivelan, S. Sabarathinam
1 citations
Chaos An Interdisciplinary Journal of Nonlinear Science
Nonlinear Dynamics and Pattern Formation
0.35
article

Investigation of transient extreme events in a mutually coupled star network of theoretical Brusselator system

I. Manimehan, K. Thamilmaran, S. V. Manivelan, S. Sabarathinam
article en
1 citations

Abstract

In this article, we present evidence of a distinct class of extreme events that occur during the transient chaotic state within network modeling using the Brusselator with a mutually coupled star network. We analyze the phenomenon of transient extreme events in the network by focusing on the lifetimes of chaotic states. These events are identified through the finite-time Lyapunov exponent and quantified using threshold and statistical methods, including the probability distribution function (PDF), generalized extreme value (GEV) distribution, and return period plots. We also evaluate the transitions of these extreme events by examining the average synchronization error and the system's energy function. Our findings, validated across networks of various sizes, demonstrate consistent patterns and behaviors, contributing to a deeper understanding of transient extreme events in complex networks.

Chaos An Interdisciplinary Journal of Nonlinear ScienceVol. 34(9)
National Research University Higher School of Economics (RU), Chennai Mathematical Institute (IN), Bharathidasan University (IN)
Affordable and clean energy
Openalex Percentile: Top 39%
Nonlinear Dynamics and Pattern Formation
0.35
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