Future Internet, Volume 15, Issue 9
September 2023 - 36 articles
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Cover Story: Digital twins enhance the intelligent manufacturing process by replicating system behavior, offering real-time analysis, and predictive maintenance during operations. Due to modeling complexity, most of them use purely data-driven approaches without considering physical phenomena. To attenuate modeling error and increase modeling interpretability, this paper proposes a novel approach, using neural ODEs and physical dynamical equations for cooling fan system digital twins. This hybrid modeling approach achieves accurate prediction with fewer parameters and is robust against unexpected input patterns. This work demonstrates great potential for adapting neural networks into a physical or mathematical framework, enabling more intelligent and robust manufacturing processes in the future. View this paper