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Emergence of rogue-like waves in a reaction-diffusion system: Stochastic output from deterministic dissipative
Edgar Knobloch1, Arik Yochelis2,3
1Department of Physics, University of California, Berkeley, California 94720, USA.
Chaos (Woodbury, N.Y.)
|May 24, 2024
Summary
Rogue wave dynamics emerge in reaction-diffusion systems due to subcritical Turing instability. Localized spikes intermittently appear, collapse, and regrow, exhibiting memoryless behavior in large domains.
Area of Science:
- Nonlinear dynamics
- Complex systems
- Mathematical modeling
Background:
- Rogue waves are nonlinear phenomena observed across diverse fields, including oceanography, optics, and Bose-Einstein condensates.
- Understanding the mechanisms behind rogue wave formation is crucial for predicting and mitigating their impact.
Purpose of the Study:
- To investigate the emergence of rogue wave-like dynamics in a reaction-diffusion system.
- To analyze the role of subcritical Turing instability in generating these dynamics.
- To characterize the spatiotemporal organization and statistical properties of the observed rogue wave phenomena.
Main Methods:
- Utilizing a reaction-diffusion model exhibiting a subcritical Turing instability.
- Analyzing the system's behavior in a regime where time-independent states are unstable.
- Characterizing the intermittent excitation, collapse, and regrowth of localized spikes.
- Examining the spatiotemporal organization and statistical properties of spike dynamics in varying domain sizes.
Main Results:
- Rogue wave-like dynamics were observed in the reaction-diffusion system.
- These dynamics arise from intermittent localized spikes that collapse and subsequently regrow.
- In sufficiently large domains, the spike dynamics were found to be consistent with a memoryless process.
Conclusions:
- Subcritical Turing instability can lead to rogue wave-like phenomena in reaction-diffusion systems.
- The observed dynamics are characterized by intermittent spiking and regrowth.
- The memoryless nature of these dynamics in large domains provides insights into their statistical behavior.
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