701 citations · 710 across the 2 of their papers we have counts for
2 papers
cs.LG2022★ 9 cited
On the Activation Function Dependence of the Spectral Bias of Neural Networks
Qingguo Hong, Jonathan W. Siegel, Qinyang Tan +1
Neural networks are universal function approximators which are known to generalize well despite being dramatically overparameterized. We study this phenomenon from the point of vie…
physics.comp-ph2022★ 701 cited
A comprehensive study of non-adaptive and residual-based adaptive sampling for physics-informed neural networks
Chenxi Wu, Min Zhu, Qinyang Tan +2
Physics-informed neural networks (PINNs) have shown to be an effective tool for solving forward and inverse problems of partial differential equations (PDEs). PINNs embed the PDEs…