Complex Physics with Graph Networks for Industrial Material Flow Simulation

Material flow simulation is essential to modern manufacturing engineering. Physically based, discrete material flow simulation is slow, and, because it is not well suited for parallelization, it does not benefit from vertical scaling of the computer infrastructure. Other methods, like based on hyperbolic partial differential equations or Navier-Stokes based continuous simulations can be significantly faster, however switching between continuous and discrete representations is not yet solved. Machine learning methods have been used recently to speed up physically based simulations. In this paper we show, that the latest advances in machine learning solutions using graph networks can be applied to speed up industrial material flow simulations while still using discrete representations for the material flow objects.