  Natural physical, chemical, and biological dynamical systems are often
complex, with heterogeneous components interacting in diverse ways. We show
that graph neural networks can be designed to jointly learn the interaction
rules and the structure of the heterogeneity from data alone. The learned
latent structure and dynamics can be used to virtually decompose the complex
system which is necessary to parameterize and infer the underlying governing
equations. We tested the approach with simulation experiments of moving
particles and vector fields that interact with each other. While our current
aim is to better understand and validate the approach with simulated data, we
anticipate it to become a generally applicable tool to uncover the governing
rules underlying complex dynamics observed in nature.
