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Machine-learning-coined noise induces energy-saving synchrony.

Jingdong Zhang1,2,3, Luan Yang2, Qunxi Zhu2,4,5

  • 1School of Mathematical Sciences, SCMS, and SCAM, <a href="https://ror.org/013q1eq08">Fudan University</a>, Shanghai 200433, China.

Physical Review. E
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Summary
This summary is machine-generated.

We developed a machine learning framework to control noise and achieve synchronization in complex systems. This framework uses energy-saving artificial noise, validated across physical and biological examples.

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Area of Science:

  • Complex Systems
  • Machine Learning
  • Nonlinear Dynamics

Background:

  • Noise-induced synchronization is common in natural and engineered systems.
  • Controlling synchronization often requires significant energy input.
  • Existing methods lack adaptability for diverse systems.

Purpose of the Study:

  • To develop a machine learning framework for noise controllers to achieve synchronization.
  • To identify and explain the energy-saving properties of the proposed framework.
  • To demonstrate the framework's effectiveness across various complex systems.

Main Methods:

  • Formulation of a machine learning framework for noise control.
  • Investigation of implicit energy regularization phenomena.
  • Rigorous elucidation of the underlying synchronization mechanism.
  • Testing across diverse physical and biological systems under realistic constraints.

Main Results:

  • The framework successfully achieves synchronization in complex systems.
  • An implicit energy regularization phenomenon was discovered, leading to energy-saving artificial noise.
  • The mechanism driving energy-saving synchronization was elucidated.
  • The framework demonstrated practical feasibility and efficacy across varied systems.

Conclusions:

  • The developed machine learning framework offers an energy-efficient approach to noise-induced synchronization.
  • The findings provide a novel mechanism for controlling synchronization in complex systems.
  • The framework's adaptability makes it suitable for diverse real-world applications.