1 citations · 3 across the 4 of their papers we have counts for
4 papers
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…
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…
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…
Densely Multiplied Physics Informed Neural Networks
Feilong Jiang, Xiaonan Hou, Min Xia
Although physics-informed neural networks (PINNs) have shown great potential in dealing with nonlinear partial differential equations (PDEs), it is common that PINNs will suffer fr…