Pu Ren
Impact in
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- Model Reduction and Neural Networks
Papers in
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- Model Reduction and Neural Networks 7
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- Structural Health Monitoring Techniques 5
- Infrastructure Maintenance and Monitoring 3
- Seismic Performance and Analysis 3
- Co-authors
- Hao Sun (9 shared papers)Yang Liu (7 shared papers)Chengping Rao (5 shared papers)Jianxun Wang (3 shared papers)Oral Büyüköztürk (1 shared paper)Xinyu Chen (1 shared paper)Lijun Sun (1 shared paper)Su Chen (1 shared paper)
- Journals
- Mechanical Systems and Signal Processing (3 papers)Computer Physics Communications (2 papers)Machine Learning Science and Technology (1 paper)Nature Machine Intelligence (1 paper)Earthquake Engineering & Structural Dynamics (1 paper)
- Partner nations
- United StatesChinaHong Kong
In The Last Decade
Pu Ren
13 papers receiving 434 citations
Pu Ren's Hit Papers
Peers
Comparison fields: 5 of 63
- Statistical and Nonlinear Physics 200
- Computational Mathematics 8
- Civil and Structural Engineering 102
- Computational Mechanics 92
- Geophysics 46
Countries citing papers authored by Pu Ren
This map shows the geographic impact of Pu Ren'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 Pu Ren with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Pu Ren more than expected).
Fields of papers citing papers by Pu Ren
This network shows the impact of papers produced by Pu Ren. 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 Pu Ren. The network helps show where Pu Ren may publish in the future.
Co-authors
The 25 scholars most cited alongside Pu Ren, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | PhyCRNet: Physics-informed convolutional-recurrent network for solving spatiotemporal PDEs Hit paper breakdown → | 2021 | 180 |
| 2 | 2023 | 75 | |
| 3 | 2023 | 53 | |
| 4 | 2021 | 52 | |
| 5 | 2023 | 29 | |
| 6 | 2022 | 20 | |
| 7 | 2018 | 19 | |
| 8 | 2023 | 11 | |
| 9 | 2023 | 3 | |
| 10 | 2025 | 3 | |
| 11 | 2024 | 2 | |
| 12 | 2024 | 1 | |
| 13 | 2025 | 1 | |
| 14 | 2025 | 0 |
About Pu Ren
Pu Ren is a scholar working on Statistical and Nonlinear Physics, Civil and Structural Engineering, Computational Mechanics, Atmospheric Science and Computer Vision and Pattern Recognition, having authored 14 papers that have together received 449 indexed citations. Recurring topics across this work include Model Reduction and Neural Networks (7 papers), Structural Health Monitoring Techniques (5 papers), Infrastructure Maintenance and Monitoring (3 papers), Seismic Performance and Analysis (3 papers), Fluid Dynamics and Vibration Analysis (3 papers), Ultrasonics and Acoustic Wave Propagation (2 papers), Meteorological Phenomena and Simulations (2 papers) and Advanced Image Processing Techniques (2 papers). The work is most often cited by research in Statistical and Nonlinear Physics (200 citations), Computational Mathematics (8 citations), Civil and Structural Engineering (102 citations), Computational Mechanics (92 citations) and Geophysics (46 citations). Pu Ren has collaborated with scholars based in United States, China and Hong Kong. Frequent co-authors include Hao Sun, Yang Liu, Chengping Rao, Jianxun Wang, Oral Büyüköztürk, Xinyu Chen, Lijun Sun, Su Chen, Changqing Miao and Hanwei Zhao. Their work appears in journals such as Mechanical Systems and Signal Processing, Computer Physics Communications, Machine Learning Science and Technology, Nature Machine Intelligence and Earthquake Engineering & Structural Dynamics.
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.