Learning 3D Granular Flow Simulations
This work addresses the difficulty of simulating physical processes in natural sciences and engineering, such as rotating drums and hoppers, but is incremental as it applies a known method to a specific domain.
The paper tackled the problem of modeling 3D granular flow simulations without first-principle solutions by using Graph Neural Networks, achieving accurate modeling of particle flows and mixing entropies compared to LIGGGHTS trajectories.
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.