5 citations · 14 across the 13 of their papers we have counts for
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math.NA2023
SPFNO: Spectral operator learning for PDEs with Dirichlet and Neumann boundary conditions
Ziyuan Liu, Yuhang Wu, Daniel Zhengyu Huang +3
Neural operators have been validated as promising deep surrogate models for solving partial differential equations (PDEs). Despite the critical role of boundary conditions in PDEs,…
cs.LG2023
On Training Derivative-Constrained Neural Networks
KaiChieh Lo, Daniel Huang
We refer to the setting where the (partial) derivatives of a neural network's (NN's) predictions with respect to its inputs are used as additional training signal as a derivative-c…
stat.ML2023★ 3 cited
Sampling via Gradient Flows in the Space of Probability Measures
Yifan Chen, Daniel Zhengyu Huang, Jiaoyang Huang +2
Sampling a target probability distribution with an unknown normalization constant is a fundamental challenge in computational science and engineering. Recent work shows that algori…