Nanzhe Wang

1.1k total citations · 1 hit paper
21 papers, 796 citations indexed

About

Nanzhe Wang is a scholar working on Ocean Engineering, Mechanical Engineering and Statistical and Nonlinear Physics. According to data from OpenAlex, Nanzhe Wang has authored 21 papers receiving a total of 796 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Ocean Engineering, 11 papers in Mechanical Engineering and 10 papers in Statistical and Nonlinear Physics. Recurrent topics in Nanzhe Wang's work include Hydraulic Fracturing and Reservoir Analysis (11 papers), Reservoir Engineering and Simulation Methods (11 papers) and Model Reduction and Neural Networks (10 papers). Nanzhe Wang is often cited by papers focused on Hydraulic Fracturing and Reservoir Analysis (11 papers), Reservoir Engineering and Simulation Methods (11 papers) and Model Reduction and Neural Networks (10 papers). Nanzhe Wang collaborates with scholars based in China, United States and Switzerland. Nanzhe Wang's co-authors include Dongxiao Zhang, Haibin Chang, Heng Li, Rui Xu, Yuntian Chen, Rong Miao, Dou Huang, Jinyue Yan, Haoran Zhang and Junsheng Zeng and has published in prestigious journals such as Water Resources Research, Journal of Computational Physics and Geophysical Research Letters.

In The Last Decade

Nanzhe Wang

21 papers receiving 771 citations

Hit Papers

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What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Deep learning of subsurface flow via theory-guided neural network

2020 223 citations Nanzhe Wang, Dongxiao Zhang et al. Journal of Hydrology profile →

Peers

Countries citing papers authored by Nanzhe Wang

Since Specialization

Citations

This map shows the geographic impact of Nanzhe Wang's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Nanzhe Wang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nanzhe Wang more than expected).

Fields of papers citing papers by Nanzhe Wang

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Nanzhe Wang. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Nanzhe Wang. The network helps show where Nanzhe Wang may publish in the future.

Co-authorship network of co-authors of Nanzhe Wang

This figure shows the co-authorship network connecting the top 25 collaborators of Nanzhe Wang. A scholar is included among the top collaborators of Nanzhe Wang based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Nanzhe Wang. Nanzhe Wang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

#	Work	Indexed citations
1	Deep learning framework for history matching CO2 storage with 4D seismic and monitoring well data 2025 ·Geoenergy Science and Engineering ·Nanzhe Wang, Louis J. Durlofsky	1
2	A comprehensive review of physics-informed deep learning and its applications in geoenergy development 2025 ·Nanzhe Wang, Yuntian Chen, Dongxiao Zhang	1
3	Physics‐Informed Convolutional Decoder (PICD): A Novel Approach for Direct Inversion of Heterogeneous Subsurface Flow 2024 ·Geophysical Research Letters ·Nanzhe Wang, Xiang‐Zhao Kong, Dongxiao Zhang	12
4	Deep-learning-based upscaling method for geologic models via theory-guided convolutional neural network 2023 ·Computational Geosciences ·Nanzhe Wang, Qinzhuo Liao, Haibin Chang, Dongxiao Zhang	5
5	Inverse Modeling for Subsurface Flow Based on Deep Learning Surrogates and Active Learning Strategies 2023 ·Water Resources Research ·Nanzhe Wang, Haibin Chang, Dongxiao Zhang	15
6	Deep learning based closed-loop well control optimization of geothermal reservoir with uncertain permeability 2023 ·Renewable Energy ·Nanzhe Wang, Haibin Chang, (unknown), Dongxiao Zhang	27
7	GANSim-surrogate: An integrated framework for stochastic conditional geomodelling 2023 ·Journal of Hydrology ·Suihong Song, Dongxiao Zhang, Tapan Mukerji, Nanzhe Wang	17
8	Uncertainty quantification and inverse modeling for subsurface flow in 3D heterogeneous formations using a theory-guided convolutional encoder-decoder network 2022 ·Journal of Hydrology ·Rui Xu, Dongxiao Zhang, Nanzhe Wang	17
9	Surrogate and inverse modeling for two-phase flow in porous media via theory-guided convolutional neural network 2022 ·Journal of Computational Physics ·Nanzhe Wang, Haibin Chang, Dongxiao Zhang	38
10	Deep-learning based discovery of partial differential equations in integral form from sparse and noisy data 2021 ·Journal of Computational Physics ·Hao Xu, Dongxiao Zhang, Nanzhe Wang	26
11	Efficient well placement optimization based on theory-guided convolutional neural network 2021 ·Journal of Petroleum Science and Engineering ·Nanzhe Wang, Haibin Chang, Dongxiao Zhang, Liang Xue, Yuntian Chen	36
12	Theory-guided hard constraint projection (HCP): A knowledge-based data-driven scientific machine learning method 2021 ·Journal of Computational Physics ·Yuntian Chen, Dou Huang, Dongxiao Zhang, Junsheng Zeng, Nanzhe Wang, Haoran Zhang, Jinyue Yan	95
13	Theory-guided Auto-Encoder for surrogate construction and inverse modeling 2021 ·Computer Methods in Applied Mechanics and Engineering ·Nanzhe Wang, Haibin Chang, Dongxiao Zhang	58
14	Weak form theory-guided neural network (TgNN-wf) for deep learning of subsurface single- and two-phase flow 2021 ·Journal of Computational Physics ·Rui Xu, Dongxiao Zhang, Rong Miao, Nanzhe Wang	50
15	Deep Learning of Two-Phase Flow in Porous Media via Theory-Guided Neural Networks 2021 ·SPE Journal ·Jian Li, Dongxiao Zhang, Nanzhe Wang, Haibin Chang	21
16	Solution of diffusivity equations with local sources/sinks and surrogate modeling using weak form Theory-guided Neural Network 2021 ·Advances in Water Resources ·Rui Xu, Nanzhe Wang, Dongxiao Zhang	17
17	Theory-guided full convolutional neural network: An efficient surrogate model for inverse problems in subsurface contaminant transport 2021 ·Advances in Water Resources ·(unknown), Nanzhe Wang, Dongxiao Zhang	29
18	Efficient Uncertainty Quantification and Data Assimilation via Theory-Guided Convolutional Neural Network 2021 ·SPE Journal ·Nanzhe Wang, Haibin Chang, Dongxiao Zhang	38
19	Deep learning of subsurface flow via theory-guided neural network breakdown → 2020 ·Journal of Hydrology ·Nanzhe Wang, Dongxiao Zhang, Haibin Chang, Heng Li	223
20	Efficient uncertainty quantification for dynamic subsurface flow with surrogate by Theory-guided Neural Network 2020 ·Computer Methods in Applied Mechanics and Engineering ·Nanzhe Wang, Haibin Chang, Dongxiao Zhang	57

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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