Detecting disturbances in network-coupled dynamical systems with machine learning

TL;DR

This paper introduces a machine learning approach to detect and identify unknown disturbances in network-coupled dynamical systems without prior knowledge of the disturbances or system dynamics, applicable to both linear and nonlinear cases.

Contribution

It presents a model-free machine learning method that can identify disturbance locations and properties using only prior observations and known forcing functions.

Findings

Successfully identifies disturbance locations in various systems

Works with both linear and nonlinear disturbances

Scalable to large networks

Abstract

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.