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Faster network disruption from layered oscillatory dynamics.

Melvyn Tyloo1

  • 1Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA and Center for Nonlinear Studies (CNLS), Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA.

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Noise correlations in complex networks can destabilize system equilibrium. This study reveals that spatial and temporal noise correlations, particularly along network eigenmodes, significantly impact escape times and network stability.

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Area of Science:

  • Complex systems
  • Network science
  • Nonlinear dynamics

Background:

  • Nonlinear complex networks possess multiple stable states.
  • Perturbations or noise can induce transitions between these states.
  • Layered complex networks can exhibit amplified fluctuations.

Purpose of the Study:

  • To investigate the impact of system-specific correlated noise on the first escape time of nonlinearly coupled oscillators.
  • To analyze how noise correlations affect network stability and functioning.

Main Methods:

  • Analysis of first escape times on synthetic networks.
  • Comparison of noise from layered dynamics with uncorrelated noise.
  • Investigation of noise correlations along the Laplacian matrix's lowest-lying eigenmodes.

Main Results:

  • Strong amplification of fluctuations poses a threat to network function.
  • Spatial and temporal correlations of noise, especially along specific eigenmodes, are critical factors.
  • Correlated noise can significantly alter the first escape time dynamics.

Conclusions:

  • Noise characteristics, beyond mere amplitude, critically influence the stability of nonlinear complex networks.
  • Understanding noise correlations is essential for predicting and controlling system behavior.
  • Layered network dynamics and associated noise patterns require careful consideration in system design.