329 citations · 939 across the 14 of their papers we have counts for
14 papers
Machine learning for sustainable geoenergy: uncertainty, physics and decision-ready inference
Hannah P. Menke, Ahmed H. Elsheikh, Lingli Wei +2
Geoenergy projects (CO2 storage, geothermal, subsurface H2 generation/storage, critical minerals from subsurface fluids, or nuclear waste disposal) increasingly follow a petroleum-…
Physics-Informed Convolutional Decoder (PICD): A novel approach for direct inversion of heterogeneous subsurface flow
Nanzhe Wang, Xiang-Zhao Kong, Dongxiao Zhang
In this study, we present the development and application of the physics-informed convolutional decoder (PICD) framework for inverse modeling of heterogenous groundwater flow. PICD…
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…
Uncertainty quantification and inverse modeling for subsurface flow in 3D heterogeneous formations using a theory-guided convolutional encoder-decoder network
Rui Xu, Dongxiao Zhang, Nanzhe Wang
We build surrogate models for dynamic 3D subsurface single-phase flow problems with multiple vertical producing wells. The surrogate model provides efficient pressure estimation of…
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 hard constraint projection (HCP): a knowledge-based data-driven scientific machine learning method
Yuntian Chen, Dou Huang, Dongxiao Zhang +4
Machine learning models have been successfully used in many scientific and engineering fields. However, it remains difficult for a model to simultaneously utilize domain knowledge…
Deep-learning based discovery of partial differential equations in integral form from sparse and noisy data
Hao Xu, Dongxiao Zhang, Nanzhe Wang
Data-driven discovery of partial differential equations (PDEs) has attracted increasing attention in recent years. Although significant progress has been made, certain unresolved i…
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…
Weak Form Theory-guided Neural Network (TgNN-wf) for Deep Learning of Subsurface Single and Two-phase Flow
Rui Xu, Dongxiao Zhang, Miao Rong +1
Deep neural networks (DNNs) are widely used as surrogate models in geophysical applications; incorporating theoretical guidance into DNNs has improved the generalizability. However…
A Lagrangian Dual-based Theory-guided Deep Neural Network
Miao Rong, Dongxiao Zhang, Nanzhe Wang
The theory-guided neural network (TgNN) is a kind of method which improves the effectiveness and efficiency of neural network architectures by incorporating scientific knowledge or…
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…
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…