Zhengwei Yang
- Materials Chemistry
- Biomedical Engineering
- Computer Vision and Pattern Recognition top 5%
- Civil and Structural Engineering top 10%
- Atmospheric Science top 10%
- Co-authors
- Siqi ShiYue LiuMaxim AvdeevDahui LiuShuchang MaZheng WangXian ZhongHao Huang
- Topics
- Machine Learning in Materials Science (10 papers)Meteorological Phenomena and Simulations (9 papers)Precipitation Measurement and Analysis (9 papers)
- Partner nations
- ChinaAustraliaUnited States
In The Last Decade
Zhengwei Yang
46 papers receiving 1.0k citations
Hit Papers
Peers
Comparison fields: 5 of 104
- Materials Chemistry 232
- Biomedical Engineering 176
- Computer Vision and Pattern Recognition 159
- Civil and Structural Engineering 158
- Atmospheric Science 154
Countries citing papers authored by Zhengwei Yang
This map shows the geographic impact of Zhengwei Yang'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 Zhengwei Yang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Zhengwei Yang more than expected).
Fields of papers citing papers by Zhengwei Yang
This network shows the impact of papers produced by Zhengwei Yang. 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 Zhengwei Yang. The network helps show where Zhengwei Yang may publish in the future.
Co-authorship network of co-authors of Zhengwei Yang
This figure shows the co-authorship network connecting the top 25 collaborators of Zhengwei Yang. A scholar is included among the top collaborators of Zhengwei Yang 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 Zhengwei Yang. Zhengwei Yang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 6 | |
| 2 | 0 | |
| 3 | 1 | |
| 4 | 0 | |
| 5 | 4 | |
| 6 | 2 | |
| 7 | 5 | |
| 8 | 5 | |
| 9 | 5 | |
| 10 | 3 | |
| 11 | 12 | |
| 12 | Data quantity governance for machine learning in materials sciencebreakdown → | 106 |
| 13 | Generative artificial intelligence and its applications in materials science: Current situation and future perspectivesbreakdown → | 164 |
| 14 | 9 | |
| 15 | 2 | |
| 16 | 19 | |
| 17 | 39 | |
| 18 | 36 | |
| 19 | 5 | |
| 20 | 18 |
About Zhengwei Yang
Zhengwei Yang is a scholar working on Computer Vision and Pattern Recognition, Atmospheric Science and Automotive Engineering, having authored 51 papers that have together received 1.1k indexed citations. Recurring topics across this work include Machine Learning in Materials Science (10 papers), Meteorological Phenomena and Simulations (9 papers) and Precipitation Measurement and Analysis (9 papers). The work is most often cited by research in Health Informatics (15 citations), Environmental Engineering (149 citations) and Atmospheric Science (154 citations). Zhengwei Yang has collaborated with scholars based in China, Australia and United States. Frequent co-authors include Siqi Shi, Yue Liu, Maxim Avdeev, Dahui Liu, Shuchang Ma, Zheng Wang, Xian Zhong, Hao Huang, Kun Zhao and Caihong Li. Their work appears in journals such as Advanced Materials, Angewandte Chemie International Edition and Advanced Functional Materials.
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.