Structural origins and real-time predictors of intermittency
A Barone1, A Carrassi1, T Savary2
1Department of Physics and Astronomy, University of Bologna, Viale Carlo Berti Pichat, 6/2, Bologna 40127, Italy.
Chaos (Woodbury, N.Y.)
|October 15, 2025
Summary
Predicting regime switches in complex systems is difficult. This study identifies common indicators, like Lyapunov vector alignment, that can forecast these intermittent transitions across various models.
Area of Science:
- Complex Systems Dynamics
- Nonlinear Dynamics
- Predictive Modeling
Background:
- Intermittency describes systems alternating between distinct states, posing prediction challenges.
- Traditional approaches focus on global statistics, overlooking real-time transition drivers.
- Applications span turbulence, climate, plasma physics, neuroscience, and economics.
Purpose of the Study:
- To investigate the local causes and real-time drivers of regime changes in intermittent systems.
- To identify common indicators and precursors of regime transitions across diverse dynamical models.
- To develop a foundation for predicting intermittent events.
Main Methods:
- Analysis of five distinct systems with varying complexity.
- Real-time monitoring of system dynamics to detect transition precursors.
- Correlation analysis between Lyapunov vector alignment and regime changes.
Main Results:
- Identified common indicators and precursors for regime transitions across different intermittency types.
- Found a consistent correlation between Lyapunov vector alignment and subsequent regime changes.
- Observed specific intermittent behaviors in the Lorenz 96 and Kuramoto-Sivashinsky models.
Conclusions:
- Common mechanisms drive intermittent behaviors across diverse systems.
- Lyapunov vector alignment serves as a general indicator for predicting regime changes.
- Findings pave the way for developing predictive tools for intermittent phenomena.
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