701 citations · 706 across the 3 of their papers we have counts for
3 papers
cs.NE2023★ 4 cited
Artificial to Spiking Neural Networks Conversion for Scientific Machine Learning
Qian Zhang, Chenxi Wu, Adar Kahana +4
We introduce a method to convert Physics-Informed Neural Networks (PINNs), commonly used in scientific machine learning, to Spiking Neural Networks (SNNs), which are expected to ha…
math.MG2022★ 1 cited
Functional dimension of feedforward ReLU neural networks
J. Elisenda Grigsby, Kathryn Lindsey, Robert Meyerhoff +1
It is well-known that the parameterized family of functions representable by fully-connected feedforward neural networks with ReLU activation function is precisely the class of pie…
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…