TY  - JOUR
AU  - Zhao, Yang
AU  - Fu, Zhongxue
AU  - Zhao, Jianfeng
PY  - 2025
TI  - Weighted physics-informed neural network (weighted PINN) for obtaining elastic responses under Hertzian-like contact
JO  - Friction
SN  - 2223-7690
SP  - 9441062
VL  - 13
IS  - 10
AB  - 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.
UR  - https://doi.org/10.26599/FRICT.2025.9441062
DO  - 10.26599/FRICT.2025.9441062