40 citations · 75 across the 3 of their papers we have counts for
4 papers
Advancing Generalization in PINNs through Latent-Space Representations
Honghui Wang, Yifan Pu, Shiji Song +1
Physics-informed neural networks (PINNs) have made significant strides in modeling dynamical systems governed by partial differential equations (PDEs). However, their generalizatio…
Learning Specialized Activation Functions for Physics-informed Neural Networks
Honghui Wang, Lu Lu, Shiji Song +1
Physics-informed neural networks (PINNs) are known to suffer from optimization difficulty. In this work, we reveal the connection between the optimization difficulty of PINNs and a…
Exploring the Equivalence of Siamese Self-Supervised Learning via A Unified Gradient Framework
Chenxin Tao, Honghui Wang, Xizhou Zhu +4
Self-supervised learning has shown its great potential to extract powerful visual representations without human annotations. Various works are proposed to deal with self-supervised…
Dynamic Neural Networks: A Survey
Yizeng Han, Gao Huang, Shiji Song +3
Dynamic neural network is an emerging research topic in deep learning. Compared to static models which have fixed computational graphs and parameters at the inference stage, dynami…