Detecting disturbances in network-coupled dynamical systems with machine
  learning
Per Sebastian Skardal Juan G. Restrepo
Identifying disturbances in network-coupled dynamical systems without
knowledge of the disturbances or underlying dynamics is a problem with a wide
range of applications. For example, one might want to know which nodes in the
network are being disturbed and identify the type of disturbance. Here we
present a model-free method based on machine learning to identify such unknown
disturbances based only on prior observations of the system when forced by a
known training function. We find that this method is able to identify the
locations and properties of many different types of unknown disturbances using
a variety of known forcing functions. We illustrate our results both with
linear and nonlinear disturbances using food web and neuronal activity models.
Finally, we discuss how to scale our method to large networks.