# Machine learning the interaction network in coupled dynamical systems

arXiv: https://arxiv.org/abs/2310.03378 · HTML: https://mlsanity.com/paper/2310.03378/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 24 references that are in the map.
- First posted on arXiv 5 Oct 2023.

## Builds on (most cited first, 5 of 5)

- [Neural Message Passing for Quantum Chemistry](https://mlsanity.com/paper/1704.01212/) (2017)
- [Categorical Reparameterization with Gumbel-Softmax](https://mlsanity.com/paper/1611.01144/) (2016)
- [The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables](https://mlsanity.com/paper/1611.00712/) (2016)
- [Interaction Networks for Learning about Objects, Relations and Physics](https://mlsanity.com/paper/1612.00222/) (2016)
- [A Compositional Object-Based Approach to Learning Physical Dynamics](https://mlsanity.com/paper/1612.00341/) (2016)

## Abstract (arXiv)

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.