329 citations · 897 across the 13 of their papers we have counts for
13 papers
Identification of Physical Processes and Unknown Parameters of 3D Groundwater Contaminant Problems via Theory-guided U-net
Tianhao He, Haibin Chang, Dongxiao Zhang
Identification of unknown physical processes and parameters of groundwater contaminant sources is a challenging task due to their ill-posed and non-unique nature. Numerous works ha…
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…
Deep-learning-based upscaling method for geologic models via theory-guided convolutional neural network
Nanzhe Wang, Qinzhuo Liao, Haibin Chang +1
Large-scale or high-resolution geologic models usually comprise a huge number of grid blocks, which can be computationally demanding and time-consuming to solve with numerical simu…
Surrogate and inverse modeling for two-phase flow in porous media via theory-guided convolutional neural network
Nanzhe Wang, Haibin Chang, Dongxiao Zhang
The theory-guided convolutional neural network (TgCNN) framework, which can incorporate discretized governing equation residuals into the training of convolutional neural networks…
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…
Deep-Learning based Inverse Modeling Approaches: A Subsurface Flow Example
Nanzhe Wang, Haibin Chang, Dongxiao Zhang
Deep-learning has achieved good performance and shown great potential for solving forward and inverse problems. In this work, two categories of innovative deep-learning based inver…
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…
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…
Identification of physical processes via combined data-driven and data-assimilation methods
Haibin Chang, Dongxiao Zhang
With the advent of modern data collection and storage technologies, data-driven approaches have been developed for discovering the governing partial differential equations (PDE) of…
Billiards in near rectangles
Haibin Chang, Yilong Yang
For every quadrilateral sufficiently close to a rectangle, we shall show that it possess a periodic billiard path. This is an REU work done at ICERM in Summer 2012.