329 citations · 336 across the 2 of their papers we have counts for
8 papers
Deep learning based closed-loop optimization of geothermal reservoir production
Nanzhe Wang, Haibin Chang, Xiangzhao Kong +2
To maximize the economic benefits of geothermal energy production, it is essential to optimize geothermal reservoir management strategies, in which geologic uncertainty should be c…
Theory-guided Auto-Encoder for Surrogate Construction and Inverse Modeling
Nanzhe Wang, Haibin Chang, Dongxiao Zhang
A Theory-guided Auto-Encoder (TgAE) framework is proposed for surrogate construction and is further used for uncertainty quantification and inverse modeling tasks. The framework is…
Efficient Uncertainty Quantification for Dynamic Subsurface Flow with Surrogate by Theory-guided Neural Network
Nanzhe Wang, Haibin Chang, Dongxiao Zhang
Subsurface flow problems usually involve some degree of uncertainty. Consequently, uncertainty quantification is commonly necessary for subsurface flow prediction. In this work, we…
DLGA-PDE: Discovery of PDEs with incomplete candidate library via combination of deep learning and genetic algorithm
Hao Xu, Haibin Chang, Dongxiao Zhang
Data-driven methods have recently been developed to discover underlying partial differential equations (PDEs) of physical problems. However, for these methods, a complete candidate…
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…
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…