  Recently, the application of machine learning models has gained momentum in
natural sciences and engineering, which is a natural fit due to the abundance
of data in these fields. However, the modeling of physical processes from
simulation data without first principle solutions remains difficult. Here, we
present a Graph Neural Networks approach towards accurate modeling of complex
3D granular flow simulation processes created by the discrete element method
LIGGGHTS and concentrate on simulations of physical systems found in real world
applications like rotating drums and hoppers. We discuss how to implement Graph
Neural Networks that deal with 3D objects, boundary conditions, particle -
particle, and particle - boundary interactions such that an accurate modeling
of relevant physical quantities is made possible. Finally, we compare the
machine learning based trajectories to LIGGGHTS trajectories in terms of
particle flows and mixing entropies.
