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.
@article{arxiv.1907.05928,
title = {A machine learning framework for computationally expensive transient models},
author = {Prashant Kumar and Kushal Sinha and Nandkishor Nere and Yujin Shin and Raimundo Ho and Ahmad Sheikh and Laurie Mlinar},
journal= {arXiv preprint arXiv:1907.05928},
year = {2019}
}