  The promise of machine learning has been explored in a variety of scientific
disciplines in the last few years, however, its application on first-principles
based computationally expensive tools is still in nascent stage. Even with the
advances in computational resources and power, transient simulations of
large-scale dynamic systems using a variety of the first-principles based
computational tools are still limited. In this work, we propose an ensemble
approach where we combine one such computationally expensive tool, called
discrete element method (DEM), with a time-series forecasting method called
auto-regressive integrated moving average (ARIMA) and machine-learning methods
to significantly reduce the computational burden while retaining model accuracy
and performance. The developed machine-learning model shows good predictability
and agreement with the literature, demonstrating its tremendous potential in
scientific computing.
