Physics-informed neural network (PINN) provides a novel method towards the understanding of mechanical behaviour in tribology contacts where the deformation of the contacting body plays a pivotal role in determining the contact scenario of dry and elastohydrodynamic lubricated (EHL) contacts. Here, we delineate the design and construction of PINN for obtaining elastic deformations under Hertzian pressure. PINN obtains the elastic deformation by transforming the linear elasticity equation into optimizing a neural network, which presents a new method towards obtaining elastic deformation in tribological contacts. Our results are consistent with finite element method’s results. Hence, we envision our method provides great application potential in dry and EHL contacts in the prediction of elastic deformations.

The last decade has witnessed a surge of interest in artificial neural network in many different areas of scientific research. Despite the rapid expansion in the application of neural networks, few efforts have been carried out to introduce such a powerful tool into lubrication studies. Thus, this work aims to apply the physics-informed neural network (PINN) to the hydrodynamic lubrication analysis. The 2D Reynolds equation is solved. The PINN is a meshless method and does not require big data for network training compared with classical methods. Our results are consistent with those obtained by experiments and the finite element method. Hence, we envision that the PINN method will have great application potential in lubrication and bearing research.