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- W3040805848 abstract "Abstract Transient 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." @default.
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- W3040805848 date "2020-07-13" @default.
- W3040805848 modified "2023-09-26" @default.
- W3040805848 title "A machine learning framework for computationally expensive transient models" @default.
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- W3040805848 doi "https://doi.org/10.1038/s41598-020-67546-w" @default.
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