The TLE-PINN method integrates EPINN and deep learning models through a transfer learning framework, combining strong physical constraints and efficient computational capabilities to accurately ...

Physics-informed neural networks (PINNs) have shown remarkable prospects in solving forward and inverse problems involving partial differential equations (PDEs). But they often stumble when ...

Rotating machinery, including critical components such as gears, bearings, and planetary systems, serves as the backbone of modern industrial equipment, ...