329 citations · 1.7k across the 32 of their papers we have counts for
32 papers
Discovery of partial differential equations from highly noisy and sparse data with physics-informed information criterion
Hao Xu, Junsheng Zeng, Dongxiao Zhang
Data-driven discovery of PDEs has made tremendous progress recently, and many canonical PDEs have been discovered successfully for proof-of-concept. However, determining the most p…
Uncertainty quantification of two-phase flow in porous media via coupled-TgNN surrogate model
Jian Li, Dongxiao Zhang, Tianhao He +1
Uncertainty quantification (UQ) of subsurface two-phase flow usually requires numerous executions of forward simulations under varying conditions. In this work, a novel coupled the…
Inferring electrochemical performance and parameters of Li-ion batteries based on deep operator networks
Qiang Zheng, Xiaoguang Yin, Dongxiao Zhang
The Li-ion battery is a complex physicochemical system that generally takes applied current as input and terminal voltage as output. The mappings from current to voltage can be des…
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…
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…
An Adaptive Deep Learning Framework for Day-ahead Forecasting of Photovoltaic Power Generation
Xing Luo, Dongxiao Zhang
Accurate forecasts of photovoltaic power generation (PVPG) are essential to optimize operations between energy supply and demand. Recently, the propagation of sensors and smart met…
Constructing Sub-scale Surrogate Model for Proppant Settling in Inclined Fractures from Simulation Data with Multi-fidelity Neural Network
Pengfei Tang, Junsheng Zeng, Dongxiao Zhang +1
Particle settling in inclined channels is an important phenomenon that occurs during hydraulic fracturing of shale gas production. Generally, in order to accurately simulate the la…
RockGPT: Reconstructing three-dimensional digital rocks from single two-dimensional slice from the perspective of video generation
Qiang Zheng, Dongxiao Zhang
Random reconstruction of three-dimensional (3D) digital rocks from two-dimensional (2D) slices is crucial for elucidating the microstructure of rocks and its effects on pore-scale…
Any equation is a forest: Symbolic genetic algorithm for discovering open-form partial differential equations (SGA-PDE)
Yuntian Chen, Yingtao Luo, Qiang Liu +2
Partial differential equations (PDEs) are concise and understandable representations of domain knowledge, which are essential for deepening our understanding of physical processes…
Deep-Learning Discovers Macroscopic Governing Equations for Viscous Gravity Currents from Microscopic Simulation Data
Junsheng Zeng, Hao Xu, Yuntian Chen +1
Although deep-learning has been successfully applied in a variety of science and engineering problems owing to its strong high-dimensional nonlinear mapping capability, it is of li…
Robust discovery of partial differential equations in complex situations
Hao Xu, Dongxiao Zhang
Data-driven discovery of partial differential equations (PDEs) has achieved considerable development in recent years. Several aspects of problems have been resolved by sparse regre…
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…
Digital rock reconstruction with user-defined properties using conditional generative adversarial networks
Qiang Zheng, Dongxiao Zhang
Uncertainty is ubiquitous with flow in subsurface rocks because of their inherent heterogeneity and lack of in-situ measurements. To complete uncertainty analysis in a multi-scale…
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…
Deep Learning of Dynamic Subsurface Flow via Theory-guided Generative Adversarial Network
Tianhao He, Dongxiao Zhang
Generative adversarial network (GAN) has been shown to be useful in various applications, such as image recognition, text processing and scientific computing, due its strong abilit…
Deep-learning of Parametric Partial Differential Equations from Sparse and Noisy Data
Hao Xu, Dongxiao Zhang, Junsheng Zeng
Data-driven methods have recently made great progress in the discovery of partial differential equations (PDEs) from spatial-temporal data. However, several challenges remain to be…
Physics-constrained indirect supervised learning
Yuntian Chen, Dongxiao Zhang
This study proposes a supervised learning method that does not rely on labels. We use variables associated with the label as indirect labels, and construct an indirect physics-cons…