A machine learning framework for computationally expensive transient models

Author:

Kumar Prashant,Sinha KushalORCID,Nere Nandkishor K.,Shin Yujin,Ho Raimundo,Mlinar Laurie B.,Sheikh Ahmad Y.

Abstract

AbstractTransient simulations of dynamic systems, using physics-based scientific computing tools, are practically limited by availability of computational resources and power. While the promise of machine learning has been explored in a variety of scientific disciplines, its application in creation of a framework for computationally expensive transient models has not been fully explored. Here, we present an ensemble approach where one such computationally expensive tool, discrete element method, is combined with time-series forecasting via auto regressive integrated moving average and machine learning methods to simulate a complex pharmaceutical problem: development of an agitation protocol in an agitated filter dryer to ensure uniform solid bed mixing. This ensemble approach leads to a significant reduction in the computational burden, while retaining model accuracy and performance, practically rendering simulations possible. The developed machine-learning model shows good predictability and agreement with the literature, demonstrating its tremendous potential in scientific computing.

Publisher

Springer Science and Business Media LLC

Subject

Multidisciplinary

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