# Model reconstruction from temporal data for coupled oscillator networks

## TL;DR

This paper explores how to reconstruct the underlying interaction network of coupled oscillators from observed transient dynamics using machine learning, enabling simultaneous identification of network structure and oscillator parameters.

## Contribution

It introduces a method to infer network topology and oscillator parameters from transient data, advancing understanding of complex coupled systems.

## Key findings

- Network topology can be reconstructed from transient dynamics.
- Machine learning effectively identifies oscillator parameters.
- Method works for arbitrary coupled oscillator networks.

## Abstract

In a complex system, the interactions between individual agents often lead to emergent collective behavior like spontaneous synchronization, swarming, and pattern formation. The topology of the network of interactions can have a dramatic influence over those dynamics. In many studies, researchers start with a specific model for both the intrinsic dynamics of each agent and the interaction network, and attempt to learn about the dynamics that can be observed in the model. Here we consider the inverse problem: given the dynamics of a system, can one learn about the underlying network? We investigate arbitrary networks of coupled phase-oscillators whose dynamics are characterized by synchronization. We demonstrate that, given sufficient observational data on the transient evolution of each oscillator, one can use machine learning methods to reconstruct the interaction network and simultaneously identify the parameters of a model for the intrinsic dynamics of the oscillators and their coupling.