Machine learning the interaction network in coupled dynamical systems

TL;DR

This paper introduces a neural network approach to infer interaction networks and predict dynamics in coupled systems using observed trajectory data, demonstrated on particle and oscillator models.

Contribution

It applies a self-supervised neural relational inference model to recover interaction networks and dynamics from trajectory data in coupled systems.

Findings

Successfully infers interaction networks from trajectory data.

Accurately predicts individual agent dynamics.

Demonstrates effectiveness on particle and oscillator systems.

Abstract

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.