Showing cs.LGShow all
3 papers · 1 filter
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…
cs.LG2024
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…