semantic_scholare-Journal of Nondestructive Testing2026-08-01
Data-Guided Physics-Informed Neural Network with Fourier Features Enhancement for Euler-Bernoulli Beam Analysis
Hailong Liu, S. Hedayatrasa, Yunpeng Zhu, Ming Cao, Yushan Yu, Dehua Zhu, et al.
TL;DR: The results demonstrate that PINN achieves more accurate and stable full-field vibration reconstructions than conventional PINNs, particularly under conditions involving high-frequency modes, and highlights the potential of hybrid data-physics neural frameworks as an efficient and reliable approach for solving complex PDE-governed dynamical systems.
Physics-informed neural networks (PINNs) have emerged as a powerful paradigm in scientific machine learning by embedding governing physical laws into neural network training through loss functions. They have demonstrated remarkable success in solving various forward and inverse p…