Yunpeng Zhu

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…Read more

Fault Diagnosis of the Rolling Bearing by a Multi-Task Deep Learning Method Based on a Classifier Generative Adversarial Network

Zhunan Shen, Xiangwei Kong, Liu Cheng, Rengen Wang, Yunpeng Zhu

Accurate fault diagnosis is essential for the safe operation of rotating machinery. Recently, traditional deep learning-based fault diagnosis have achieved promising results. However, most of these methods focus only on supervised learning and tend to use small convolution kernel…Read more