Jiang-Zhou Peng

514 total citations
27 papers, 380 citations indexed

About

Jiang-Zhou Peng is a scholar working on Computational Mechanics, Statistical and Nonlinear Physics and Aerospace Engineering. According to data from OpenAlex, Jiang-Zhou Peng has authored 27 papers receiving a total of 380 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Computational Mechanics, 13 papers in Statistical and Nonlinear Physics and 8 papers in Aerospace Engineering. Recurrent topics in Jiang-Zhou Peng's work include Model Reduction and Neural Networks (13 papers), Fluid Dynamics and Turbulent Flows (10 papers) and Heat Transfer and Optimization (6 papers). Jiang-Zhou Peng is often cited by papers focused on Model Reduction and Neural Networks (13 papers), Fluid Dynamics and Turbulent Flows (10 papers) and Heat Transfer and Optimization (6 papers). Jiang-Zhou Peng collaborates with scholars based in China and United States. Jiang-Zhou Peng's co-authors include Wei‐Tao Wu, Zhihua Chen, Nadine Aubry, Siheng Chen, Yue Hua, Xianglei Liu, Mei Mei, Yubai Li, Yong He and Qiang Zhao and has published in prestigious journals such as International Journal of Heat and Mass Transfer, Physics of Fluids and Engineering Structures.

In The Last Decade

Jiang-Zhou Peng

26 papers receiving 375 citations

Peers

Jiang-Zhou Peng

SNP

Miguel Alfonso Mendez Belgium

Hasan Güneş Türkiye

Mohammad Amin Nabian United States

Charbel Bou‐Mosleh Lebanon

Yiyang Sun United States

Vamshi Korivi United States

Rustam Mullyadzhanov Russia

Zhaohui Luo China

Yuta Ozawa Japan

Shanwu Li United States

Miguel Alfonso Mendez Belgium

Jiang-Zhou Peng

313 ×1.5

94 ×0.5

SNP

153 ×1.3

75 ×0.7

40 ×0.8

Citations per year, relative to Jiang-Zhou Peng Jiang-Zhou Peng (= 1×) peers Miguel Alfonso Mendez

Countries citing papers authored by Jiang-Zhou Peng

Since Specialization

Citations

This map shows the geographic impact of Jiang-Zhou Peng'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 Jiang-Zhou Peng with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jiang-Zhou Peng more than expected).

Fields of papers citing papers by Jiang-Zhou Peng

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jiang-Zhou Peng. 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 Jiang-Zhou Peng. The network helps show where Jiang-Zhou Peng may publish in the future.

Co-authorship network of co-authors of Jiang-Zhou Peng

This figure shows the co-authorship network connecting the top 25 collaborators of Jiang-Zhou Peng. A scholar is included among the top collaborators of Jiang-Zhou Peng 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 Jiang-Zhou Peng. Jiang-Zhou Peng 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

Peng, Jiang-Zhou, Lei Pan, Mingyang Wang, et al.. (2025). Self-adaptive graph neural network for predicting blast-induced damage in RC columns across multiple scenarios. Engineering Structures. 337. 120505–120505. 1 indexed citations

Wang, Zhiqiao, Yue Hua, Jiang-Zhou Peng, et al.. (2025). Sparse data-driven modelling of geometry adaptive heat transfer in multichip modules with model-agnostic meta-learning algorithm. International Journal of Heat and Fluid Flow. 117. 110061–110061.

Wang, Zhiqiao, Jiang-Zhou Peng, Jie Hu, et al.. (2025). BlastGraphNet: An Intelligent Computational Method for the Precise and Rapid Prediction of Blast Loads on Complex 3D Buildings Using Graph Neural Networks. Engineering. 49. 205–224. 2 indexed citations

Peng, Jiang-Zhou, Yue Hua, Nadine Aubry, et al.. (2024). Data and physics-driven modeling for fluid flow with a physics-informed graph convolutional neural network. Ocean Engineering. 301. 117551–117551. 8 indexed citations

Xie, Haoran, Yue Hua, Yubai Li, et al.. (2024). Estimation of sequential transient flow around cylinders using recurrent neural network coupled graph convolutional network. Ocean Engineering. 293. 116684–116684. 2 indexed citations

Feng, Feng, Yubai Li, Zhihua Chen, et al.. (2024). Graph convolution network-based surrogate model for natural convection in annuli. Case Studies in Thermal Engineering. 57. 104330–104330. 1 indexed citations

Wu, Wei‐Tao, et al.. (2024). Fast Spatiotemporal Sequence Graph Convolutional Network-based transient flow prediction around different airfoils. Physics of Fluids. 36(10). 3 indexed citations

Peng, Jiang-Zhou, et al.. (2023). Control policy transfer of deep reinforcement learning based intelligent forced heat convection control. International Journal of Thermal Sciences. 195. 108618–108618. 5 indexed citations

Peng, Jiang-Zhou, et al.. (2023). Computationally effective estimation of supersonic flow field around airfoils using sparse convolutional neural network. Fluid Dynamics Research. 55(3). 35504–35504. 4 indexed citations

10.

Peng, Jiang-Zhou, et al.. (2023). Prediction of internal and external flow with sparse convolution neural network: A computationally effective reduced-order model. Physics of Fluids. 35(2). 14 indexed citations

11.

Peng, Jiang-Zhou, Nadine Aubry, Yubai Li, et al.. (2023). Physics-informed graph convolutional neural network for modeling geometry-adaptive steady-state natural convection. International Journal of Heat and Mass Transfer. 216. 124593–124593. 27 indexed citations

12.

Feng, Feng, et al.. (2023). Rapid optimization for inner thermal layout in horizontal annuli using genetic algorithm coupled graph convolutional neural network. International Communications in Heat and Mass Transfer. 150. 107210–107210. 4 indexed citations

13.

Hua, Yue, et al.. (2022). Real-Time Prediction of Transarterial Drug Delivery Based on a Deep Convolutional Neural Network. Applied Sciences. 12(20). 10554–10554. 4 indexed citations

14.

Hua, Yue, Jiang-Zhou Peng, Zhifu Zhou, et al.. (2022). Thermal Performance in Convection Flow of Nanofluids Using a Deep Convolutional Neural Network. Energies. 15(21). 8195–8195. 12 indexed citations

15.

Zhao, Qiang, et al.. (2022). Reduced order modelling of natural convection of nanofluids in horizontal annular pipes based on deep learning. International Communications in Heat and Mass Transfer. 138. 106361–106361. 16 indexed citations

16.

Hua, Yue, et al.. (2022). Thermal Performance Estimation of Nanofluid-Filled Finned Absorber Tube Using Deep Convolutional Neural Network. Applied Sciences. 12(21). 10883–10883. 5 indexed citations

17.

Peng, Jiang-Zhou, Xianglei Liu, Nadine Aubry, Zhihua Chen, & Wei‐Tao Wu. (2021). Data-driven modeling of geometry-adaptive steady heat conduction based on convolutional neural networks. Case Studies in Thermal Engineering. 28. 101651–101651. 36 indexed citations

18.

Peng, Jiang-Zhou, et al.. (2020). Research on Penetration Behavior and after effects of Coated Reactive Fragments Impacting Steel targets. The International Journal of Multiphysics. 14(1). 1 indexed citations

19.

Peng, Jiang-Zhou, Siheng Chen, Nadine Aubry, Zhihua Chen, & Wei‐Tao Wu. (2020). Time-variant prediction of flow over an airfoil using deep neural network. Physics of Fluids. 32(12). 55 indexed citations

20.

Guo, Sheng, et al.. (2014). Novel method for determining the dynamic friction coefficient of explosives. Combustion Explosion and Shock Waves. 50(1). 118–123. 2 indexed citations

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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