  We investigate the use of reduced-order modelling to run discrete element
simulations at higher speeds. Taking a data-driven approach, we run many
offline simulations in advance and train a model to predict the velocity field
from the mass distribution and system control signals. Rapid model inference of
particle velocities replaces the intense process of computing contact forces
and velocity updates. In coupled DEM and multibody system simulation the
predictor model can be trained to output the interfacial reaction forces as
well. An adaptive model order reduction technique is investigated, decomposing
the media in domains of solid, liquid, and gaseous state. The model reduction
is applied to solid and liquid domains where the particle motion is strongly
correlated with the mean flow, while resolved DEM is used for gaseous domains.
Using a ridge regression predictor, the performance is tested on simulations of
a pile discharge and bulldozing. The measured accuracy is about 90% and 65%,
respectively, and the speed-up range between 10 and 60.
