Kejun Tang
Impact in
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- Model Reduction and Neural Networks
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- Tensor decomposition and applications
Papers in
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- Model Reduction and Neural Networks 6
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- Tensor decomposition and applications 1
- Co-authors
- Xiaoliang Wan (3 shared papers)Chao Yang (2 shared papers)Qifeng Liao (5 shared papers)Ke Li (2 shared papers)Jinglai Li (1 shared paper)Chao Yang (1 shared paper)Yang Li (1 shared paper)Pingqiang Zhou (1 shared paper)
- Journals
- Journal of Computational Physics (3 papers)IEEE Access (2 papers)Computer Modeling in Engineering & Sciences (1 paper)Journal of Materials Engineering and Performance (1 paper)Journal of Scientific Computing (1 paper)
- Partner nations
- ChinaUnited StatesUnited Kingdom
In The Last Decade
Kejun Tang
9 papers receiving 284 citations
Kejun Tang's Hit Papers
Peers
Comparison fields: 5 of 28
- Statistical and Nonlinear Physics 258
- Computational Mathematics 4
- Modeling and Simulation 6
- Statistics, Probability and Uncertainty 9
- Numerical Analysis 4
Countries citing papers authored by Kejun Tang
This map shows the geographic impact of Kejun Tang'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 Kejun Tang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kejun Tang more than expected).
Fields of papers citing papers by Kejun Tang
This network shows the impact of papers produced by Kejun Tang. 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 Kejun Tang. The network helps show where Kejun Tang may publish in the future.
Co-authors
The 13 scholars most cited alongside Kejun Tang, 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 | DAS-PINNs: A deep adaptive sampling method for solving high-dimensional partial differential equations Hit paper breakdown → | 2022 | 133 |
| 2 | 2019 | 95 | |
| 3 | 2022 | 29 | |
| 4 | 2024 | 12 | |
| 5 | 2019 | 9 | |
| 6 | 2024 | 7 | |
| 7 | 2020 | 3 | |
| 8 | 2025 | 2 | |
| 9 | 2022 | 1 |
About Kejun Tang
Kejun Tang is a scholar working on Statistical and Nonlinear Physics, Computational Mathematics, Statistics, Probability and Uncertainty, Mechanics of Materials and Nuclear Energy and Engineering, having authored 9 papers that have together received 291 indexed citations. Recurring topics across this work include Model Reduction and Neural Networks (6 papers), Mechanical Behavior of Composites (1 paper), Probabilistic and Robust Engineering Design (1 paper) and Tensor decomposition and applications (1 paper). The work is most often cited by research in Statistical and Nonlinear Physics (258 citations), Computational Mathematics (4 citations), Modeling and Simulation (6 citations), Statistics, Probability and Uncertainty (9 citations) and Numerical Analysis (4 citations). Kejun Tang has collaborated with scholars based in China, United States and United Kingdom. Frequent co-authors include Xiaoliang Wan, Chao Yang, Qifeng Liao, Ke Li, Jinglai Li, Chao Yang, Yang Li, Pingqiang Zhou, Yonggang Tong and Xiubing Liang. Their work appears in journals such as Journal of Computational Physics, IEEE Access, Computer Modeling in Engineering & Sciences, Journal of Materials Engineering and Performance and Journal of Scientific Computing.
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.