Dongxiao Zhang

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Featured Co-authors

Qiang Liu
190 publications
Jian Li
150 publications
Hao Xu
78 publications
Rui Xu
53 publications
Haoran Zhang
48 publications
Heng Li
30 publications
Wenbo Hu
21 publications
Yuntian Chen
15 publications
Yingtao Luo
10 publications
Nanzhe Wang
9 publications
Haibin Chang
9 publications

research

∙ 09/14/2023

Physics-constrained robust learning of open-form PDEs from limited and noisy data

Unveiling the underlying governing equations of nonlinear dynamic system...

0 Mengge Du, et al. ∙

research

∙ 11/07/2022

Retention Time Prediction for Chromatographic Enantioseparation by Quantile Geometry-enhanced Graph Neural Network

A new research framework is proposed to incorporate machine learning tec...

0 Hao Xu, et al. ∙

research

∙ 10/05/2022

TgDLF2.0: Theory-guided deep-learning for electrical load forecasting via Transformer and transfer learning

Electrical energy is essential in today's society. Accurate electrical l...

0 Jiaxin Gao, et al. ∙

research

∙ 10/04/2022

DISCOVER: Deep identification of symbolic open-form PDEs via enhanced reinforcement-learning

The working mechanisms of complex natural systems tend to abide by conci...

0 Mengge Du, et al. ∙

research

∙ 08/05/2022

Discovery of partial differential equations from highly noisy and sparse data with physics-informed information criterion

Data-driven discovery of PDEs has made tremendous progress recently, and...

0 Hao Xu, et al. ∙

research

∙ 06/20/2022

Interpretable machine learning optimization (InterOpt) for operational parameters: a case study of highly-efficient shale gas development

An algorithm named InterOpt for optimizing operational parameters is pro...

0 Yuntian Chen, et al. ∙

research

∙ 05/28/2022

Uncertainty quantification of two-phase flow in porous media via coupled-TgNN surrogate model

Uncertainty quantification (UQ) of subsurface two-phase flow usually req...

0 Jian Li, et al. ∙

research

∙ 05/11/2022

AutoKE: An automatic knowledge embedding framework for scientific machine learning

Imposing physical constraints on neural networks as a method of knowledg...

16 Mengge Du, et al. ∙

research

∙ 05/06/2022

Inferring electrochemical performance and parameters of Li-ion batteries based on deep operator networks

The Li-ion battery is a complex physicochemical system that generally ta...

0 Qiang Zheng, et al. ∙

research

∙ 04/30/2022

Identification of Physical Processes and Unknown Parameters of 3D Groundwater Contaminant Problems via Theory-guided U-net

Identification of unknown physical processes and parameters of groundwat...

0 Tianhao He, et al. ∙

research

∙ 04/16/2022

Semantic interpretation for convolutional neural networks: What makes a cat a cat?

The interpretability of deep neural networks has attracted increasing at...

207 Hao Xu, et al. ∙

research

∙ 04/15/2022

Deep learning based closed-loop optimization of geothermal reservoir production

To maximize the economic benefits of geothermal energy production, it is...

0 Nanzhe Wang, et al. ∙

research

∙ 12/31/2021

Deep-learning-based upscaling method for geologic models via theory-guided convolutional neural network

Large-scale or high-resolution geologic models usually comprise a huge n...

49 Nanzhe Wang, et al. ∙

research

∙ 11/14/2021

Uncertainty quantification and inverse modeling for subsurface flow in 3D heterogeneous formations using a theory-guided convolutional encoder-decoder network

We build surrogate models for dynamic 3D subsurface single-phase flow pr...

11 Rui Xu, et al. ∙

research

∙ 09/28/2021

An Adaptive Deep Learning Framework for Day-ahead Forecasting of Photovoltaic Power Generation

Accurate forecasts of photovoltaic power generation (PVPG) are essential...

11 Xing Luo, et al. ∙

research

∙ 09/25/2021

Constructing Sub-scale Surrogate Model for Proppant Settling in Inclined Fractures from Simulation Data with Multi-fidelity Neural Network

Particle settling in inclined channels is an important phenomenon that o...

1 Pengfei Tang, et al. ∙

research

∙ 08/05/2021

RockGPT: Reconstructing three-dimensional digital rocks from single two-dimensional slice from the perspective of video generation

Random reconstruction of three-dimensional (3D) digital rocks from two-d...

0 Qiang Zheng, et al. ∙

research

∙ 06/09/2021

Any equation is a forest: Symbolic genetic algorithm for discovering open-form partial differential equations (SGA-PDE)

Partial differential equations (PDEs) are concise and understandable rep...

1 Yuntian Chen, et al. ∙

research

∙ 05/31/2021

Deep-Learning Discovers Macroscopic Governing Equations for Viscous Gravity Currents from Microscopic Simulation Data

Although deep-learning has been successfully applied in a variety of sci...

0 Junsheng Zeng, et al. ∙

research

∙ 05/31/2021

Robust discovery of partial differential equations in complex situations

Data-driven discovery of partial differential equations (PDEs) has achie...

0 Hao Xu, et al. ∙

research

∙ 12/11/2020

Theory-guided hard constraint projection (HCP): a knowledge-based data-driven scientific machine learning method

Machine learning models have been successfully used in many scientific a...

0 Yuntian Chen, et al. ∙

research

∙ 11/24/2020

Deep-learning based discovery of partial differential equations in integral form from sparse and noisy data

Data-driven discovery of partial differential equations (PDEs) has attra...

0 Hao Xu, et al. ∙

research

∙ 11/17/2020

Theory-guided Auto-Encoder for Surrogate Construction and Inverse Modeling

A Theory-guided Auto-Encoder (TgAE) framework is proposed for surrogate ...

1 Nanzhe Wang, et al. ∙

research

∙ 09/08/2020

Weak Form Theory-guided Neural Network (TgNN-wf) for Deep Learning of Subsurface Single and Two-phase Flow

Deep neural networks (DNNs) are widely used as surrogate models in geoph...

0 Rui Xu, et al. ∙

research

∙ 08/24/2020

A Lagrangian Dual-based Theory-guided Deep Neural Network

The theory-guided neural network (TgNN) is a kind of method which improv...

7 Miao Rong, et al. ∙

research

∙ 07/28/2020

Deep-Learning based Inverse Modeling Approaches: A Subsurface Flow Example

Deep-learning has achieved good performance and shown great potential fo...

0 Nanzhe Wanga, et al. ∙

research

∙ 05/16/2020

Deep-learning of Parametric Partial Differential Equations from Sparse and Noisy Data

Data-driven methods have recently made great progress in the discovery o...

0 Hao Xu, et al. ∙

research

∙ 04/26/2020

Physics-constrained indirect supervised learning

This study proposes a supervised learning method that does not rely on l...

0 Yuntian Chen, et al. ∙

research

∙ 04/26/2020

Ensemble long short-term memory (EnLSTM) network

In this study, we propose an ensemble long short-term memory (EnLSTM) ne...

0 Yuntian Chen, et al. ∙

research

∙ 04/25/2020

Efficient Uncertainty Quantification for Dynamic Subsurface Flow with Surrogate by Theory-guided Neural Network

Subsurface flow problems usually involve some degree of uncertainty. Con...

0 Nanzhe Wang, et al. ∙

research

∙ 01/21/2020

DLGA-PDE: Discovery of PDEs with incomplete candidate library via combination of deep learning and genetic algorithm

Data-driven methods have recently been developed to discover underlying ...

0 Hao Xu, et al. ∙

research

∙ 08/13/2019

DL-PDE: Deep-learning based data-driven discovery of partial differential equations from discrete and noisy data

In recent years, data-driven methods have been utilized to learn dynamic...

5 Hao Xu, et al. ∙

research

∙ 08/03/2019

Ensemble Neural Networks (ENN): A gradient-free stochastic method

In this study, an efficient stochastic gradient-free method, the ensembl...

15 Yuntian Chen, et al. ∙

research

∙ 10/29/2018

Identification of physical processes via combined data-driven and data-assimilation methods

With the advent of modern data collection and storage technologies, data...

0 Haibin Chang, et al. ∙

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