3 papers
cs.LG2025
Mask-PINNs: Mitigating Internal Covariate Shift in Physics-Informed Neural Networks
Feilong Jiang, Xiaonan Hou, Jianqiao Ye +1
Physics-Informed Neural Networks (PINNs) have emerged as a powerful framework for solving partial differential equations (PDEs) by embedding physical laws directly into the loss fu…
cs.LG2024
Element-wise Multiplication Based Deeper Physics-Informed Neural Networks
Feilong Jiang, Xiaonan Hou, Min Xia
As a promising framework for resolving partial differential equations (PDEs), Physics-Informed Neural Networks (PINNs) have received widespread attention from industrial and scient…
eess.SY2024
Spatio-temporal Attention-based Hidden Physics-informed Neural Network for Remaining Useful Life Prediction
Feilong Jiang, Xiaonan Hou, Min Xia
Predicting the Remaining Useful Life (RUL) is essential in Prognostic Health Management (PHM) for industrial systems. Although deep learning approaches have achieved considerable s…