@article{Zhao2025, 
author = {Yang Zhao and Zhongxue Fu and Jianfeng Zhao},
title = {Weighted physics-informed neural network (weighted PINN) for obtaining elastic responses under Hertzian-like contact},
year = {2025},
journal = {Friction},
volume = {13},
number = {10},
pages = {9441062},
keywords = {physics-informed neural network (PINN), Hertzian contact, elastic deformation, elastic mechanics},
url = {https://www.sciopen.com/article/10.26599/FRICT.2025.9441062},
doi = {10.26599/FRICT.2025.9441062},
abstract = {Physics-informed neural network (PINN) provides a novel method for understanding the mechanical behavior of tribology contacts, and 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 the PINN for obtaining elastic deformations under Hertzian pressure. The PINN obtains the elastic deformation by transforming the linear elasticity equation into an optimized neural network, which presents a new method for obtaining elastic deformation in tribological contacts. Our results are consistent with the results from finite element method. Hence, we envision that our method has great application potential in dry and EHL contacts in the prediction of elastic deformation.}
}