The application of data mining technology has intensively advanced tribology research. While recent lubrication studies have highlighted the importance of data mining, researchers have not fully bridged the gap between massive lubrication data and intrinsic lubrication mechanisms. Thus, by revisiting lubrication modelling from the data-driven and physics-informed perspectives, we aim to construct a hybrid approach for hydrodynamic lubrication classification and prediction, where data-driven methods are combined with physics-informed approaches to achieve a fast and accurate prediction of the hydrodynamic lubrication scenario. Our approach will spur the application of data mining methods in lubrication studies.
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