329 citations · 897 across the 13 of their papers we have counts for
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cs.LG2019★ 329 cited
Deep Learning of Subsurface Flow via Theory-guided Neural Network
Nanzhe Wang, Dongxiao Zhang, Haibin Chang +1
Active researches are currently being performed to incorporate the wealth of scientific knowledge into data-driven approaches (e.g., neural networks) in order to improve the latter…
stat.ML2019★ 57 cited
Ensemble Neural Networks (ENN): A gradient-free stochastic method
Yuntian Chen, Haibin Chang, Meng Jin +1
In this study, an efficient stochastic gradient-free method, the ensemble neural networks (ENN), is developed. In the ENN, the optimization process relies on covariance matrices ra…
stat.ML2019★ 36 cited
DL-PDE: Deep-learning based data-driven discovery of partial differential equations from discrete and noisy data
Hao Xu, Haibin Chang, Dongxiao Zhang
In recent years, data-driven methods have been developed to learn dynamical systems and partial differential equations (PDE). The goal of such work is discovering unknown physics a…