Machine learning the interaction network in coupled dynamical systems
This work addresses the long-standing challenge of network inference in interacting systems, which is relevant for fields like physics and engineering, but it is incremental as it applies an existing method to new data.
The authors tackled the problem of inferring interaction networks and predicting dynamics in coupled dynamical systems from observed trajectory data, achieving recovery of the interaction network and prediction of individual agent dynamics using a self-supervised neural network model.
The study of interacting dynamical systems continues to attract research interest in various fields of science and engineering. In a collection of interacting particles, the interaction network contains information about how various components interact with one another. Inferring the information about the interaction network from the dynamics of agents is a problem of long-standing interest. In this work, we employ a self-supervised neural network model to achieve two outcomes: to recover the interaction network and to predict the dynamics of individual agents. Both these information are inferred solely from the observed trajectory data. This work presents an application of the Neural Relational Inference model to two dynamical systems: coupled particles mediated by Hooke's law interaction and coupled phase (Kuramoto) oscillators.