Yun Teng
- Computational Mechanics top 10%
- Computer Vision and Pattern Recognition
- Civil and Structural Engineering
- Computer Graphics and Computer-Aided Design top 5%
- Mechanical Engineering
- Co-authors
- Theodore KimMiguel Á. OtaduyHongli LiuDavid I. W. LevinJianwei LiuTony DeRoseMark MeyerZiji Ma
- Topics
- Railway Engineering and Dynamics (5 papers)Infrastructure Maintenance and Monitoring (4 papers)Structural Health Monitoring Techniques (3 papers)
- Cited by
- Computer Graphics and Computer-Aided DesignComputational MechanicsComputer Vision and Pattern Recognition
- Partner nations
- ChinaUnited StatesAustralia
In The Last Decade
Yun Teng
18 papers receiving 242 citations
Peers
Comparison fields: 5 of 58
- Computational Mechanics 98
- Computer Vision and Pattern Recognition 57
- Civil and Structural Engineering 53
- Computer Graphics and Computer-Aided Design 52
- Mechanical Engineering 45
Countries citing papers authored by Yun Teng
This map shows the geographic impact of Yun Teng'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 Yun Teng with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yun Teng more than expected).
Fields of papers citing papers by Yun Teng
This network shows the impact of papers produced by Yun Teng. 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 Yun Teng. The network helps show where Yun Teng may publish in the future.
Co-authorship network of co-authors of Yun Teng
This figure shows the co-authorship network connecting the top 25 collaborators of Yun Teng. A scholar is included among the top collaborators of Yun Teng 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 Yun Teng. Yun Teng is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 7 | |
| 3 | 19 | |
| 4 | 1 | |
| 5 | 4 | |
| 6 | 27 | |
| 7 | 18 | |
| 8 | 1 | |
| 9 | 2 | |
| 10 | 39 | |
| 11 | 38 | |
| 12 | 2 | |
| 13 | 41 | |
| 14 | 0 | |
| 15 | 0 | |
| 16 | 7 | |
| 17 | 2 | |
| 18 | 8 | |
| 19 | 1 | |
| 20 | 24 |
About Yun Teng
Yun Teng is a scholar working on Computer Graphics and Computer-Aided Design, General Decision Sciences and Computer Vision and Pattern Recognition, having authored 21 papers that have together received 246 indexed citations. Recurring topics across this work include Railway Engineering and Dynamics (5 papers), Infrastructure Maintenance and Monitoring (4 papers) and Structural Health Monitoring Techniques (3 papers). The work is most often cited by research in Computer Graphics and Computer-Aided Design (52 citations), Computational Mechanics (98 citations) and Computer Vision and Pattern Recognition (57 citations). Yun Teng has collaborated with scholars based in China, United States and Australia. Frequent co-authors include Theodore Kim, Miguel Á. Otaduy, Hongli Liu, David I. W. Levin, Jianwei Liu, Tony DeRose, Mark Meyer, Ziji Ma, Jianwei Liu and Chao Wang. Their work appears in journals such as Gene, ACM Transactions on Graphics and IEEE Transactions on Intelligent Transportation Systems.
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.