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Area of Science:

  • Complex Systems
  • Network Science
  • Machine Learning

Background:

  • Inferring dynamics of oscillator networks is challenging without explicit equations.
  • Data-driven approaches are needed for complex system analysis.

Purpose of the Study:

  • To develop a machine learning technique for predicting dynamical states in star-structured oscillator networks.
  • To utilize a parameter-aware reservoir computing scheme for efficient learning.

Main Methods:

  • Employed echo-state network (ESN) framework, a type of reservoir computing.
  • Utilized topological symmetry of the star network to minimize training cost.
  • Used a minimal setup with only two ESN units to learn parameter-dependent dynamics.

Main Results:

  • Successfully predicted multi-stable dynamics across varied coupling strengths.
  • Demonstrated efficient prediction of unseen attractors like chimera, coherent, incoherent, and cluster synchronization.
  • Validated performance for both identical and non-identical central and peripheral oscillators.

Conclusions:

  • The proposed reservoir computing framework efficiently learns dynamics of large-scale oscillator networks.
  • The method is effective even with limited training data.
  • This data-driven approach offers a powerful tool for analyzing complex network behaviors.