A machine learning framework for computationally expensive transient models
This addresses computational limitations in scientific computing for researchers using first-principles based tools, though it appears incremental as it combines existing methods.
The authors tackled the problem of computationally expensive transient simulations by proposing an ensemble approach combining discrete element method (DEM) with ARIMA and machine learning methods, achieving significant computational burden reduction while maintaining model accuracy.
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.