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Detecting disturbances in network-coupled dynamical systems with machine learning.

Per Sebastian Skardal1, Juan G Restrepo2

  • 1Department of Mathematics, Trinity College, Hartford, Connecticut 06106, USA.

Chaos (Woodbury, N.Y.)
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Summary
This summary is machine-generated.

This study introduces a machine learning method to detect unknown disturbances in network systems. The model-free approach identifies disturbance locations and types using prior system observations and known forcing functions.

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Area of Science:

  • Complex Systems
  • Network Science
  • Machine Learning

Background:

  • Identifying disturbances in network-coupled dynamical systems is crucial for many applications.
  • Current methods often require knowledge of the disturbances or system dynamics.

Purpose of the Study:

  • To develop a model-free machine learning method for identifying unknown disturbances in network systems.
  • To determine the locations and properties of various disturbance types.

Main Methods:

  • Utilized prior observations of the system under known training functions.
  • Employed a machine learning approach that does not require prior knowledge of system dynamics or disturbances.

Main Results:

  • Successfully identified locations and properties of diverse linear and nonlinear disturbances.
  • Demonstrated efficacy on food web and neuronal activity models.
  • Validated the method's ability to use various known forcing functions.

Conclusions:

  • The proposed model-free method effectively identifies unknown disturbances in network systems.
  • The approach is versatile and applicable to different system types and disturbance characteristics.
  • Strategies for scaling the method to large networks were discussed.