Machine learning based prediction of dynamical clustering in granular gases

When dense granular gases are continuously excited under microgravity conditions, spatial inhomogeneities of the particle number density can emerge. A significant share of particles may collect in strongly overpopulated regions, called clusters. This dynamical clustering, or gas-cluster transition, is caused by a complex interplay and balance between the energy influx and dissipation in particle collisions. Particle number density, container geometry, and excitation strength influence this transition. We perform Discrete Element Method (DEM) simulations for ensembles of frictional spheres in a cuboid container and apply the Kolmogorov Smirnov test and a caging criterion to the local packing fraction profiles to detect clusters. Machine learning can be used to study the gas-cluster transition, and can be a promising alternative to identify the state of the system for a given set of system parameters without time-consuming complex DEM simulations. We test various machine learning models and identify the best models to predict dynamical clustering of frictional spheres in a specific experimental geometry.