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Local Predictors of Explosive Synchronization with Ordinal Methods.

I Leyva1,2, Juan A Almendral1,2, Christophe Letellier3

  • 1Complex Systems Group & GISC, Universidad Rey Juan Carlos, 28933 Móstoles, Spain.

Entropy (Basel, Switzerland)
|February 26, 2025
PubMed
Summary
This summary is machine-generated.

Ordinal pattern transition (OPT) entropy predicts explosive synchronization in complex networks. This new measure outperforms traditional early warning signals (EWS) for critical transition prediction.

Keywords:
chaotic synchronizationcomplex networksearly warning signalsordinal patternsordinal permutation entropyordinal transition network

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Area of Science:

  • Complex systems dynamics
  • Network science
  • Nonlinear dynamics and chaos theory

Background:

  • Predicting critical transitions in complex systems is crucial.
  • Traditional early warning signals (EWS) have limitations.
  • Synchronization phenomena occur in diverse dynamical networks.

Purpose of the Study:

  • To introduce Ordinal Pattern Transition (OPT) entropy as a novel predictor of explosive synchronization.
  • To evaluate OPT entropy's effectiveness against established EWS.
  • To demonstrate OPT entropy's applicability across various complex network systems.

Main Methods:

  • Calculating OPT entropy at sentinel central nodes.
  • Analyzing networks of diffusively coupled phase oscillators and Rössler systems.
  • Investigating neural networks of Chialvo maps in star and scale-free configurations.
  • Applying OPT entropy to time series data from chaotic electronic circuits.

Main Results:

  • OPT entropy successfully predicted explosive transitions to synchronization.
  • OPT entropy demonstrated superior performance compared to traditional EWS.
  • The measure proved effective across different network topologies and system types.

Conclusions:

  • OPT entropy is a valuable new tool for predicting critical transitions in complex dynamical networks.
  • This entropic measure offers enhanced predictive power for synchronization phenomena.
  • The findings support the broader applicability of OPT entropy in network dynamics research.