Application of neural ordinary differential equations to the prediction of multi-agent systems

Dynamic systems are usually described by differential equations, but formulating these equations requires a high level of expertise and a detailed understanding of the observed system to be modelled. In this work, we present a data-driven approach, which tries to find a parameterization of neural differential equations system to describe the underlying dynamic of the observed data. The presented method is applied to a multi-agent system with thousand agents.