Detecting disturbances in network-coupled dynamical systems with machine learning
This addresses a problem for applications like monitoring networks in ecology or neuroscience, but appears incremental as it builds on existing model-free approaches.
The paper tackled the problem of identifying unknown disturbances in network-coupled dynamical systems without prior knowledge of the disturbances or underlying dynamics, and found that a model-free machine learning method could identify disturbance locations and properties using known training functions, as demonstrated with linear and nonlinear disturbances in food web and neuronal activity models.
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.