Song Cheng
- Atomic and Molecular Physics, and Optics top 10%
- Artificial Intelligence top 5%
- Statistical and Nonlinear Physics top 10%
- Computational Mathematics top 2%
- Condensed Matter Physics top 10%
- Co-authors
- Lei WangJing ChenTao XiangHaidong XiePan ZhangPengxiang XuXi‐Wen GuanYongxiang Liu
- Topics
- Quantum many-body systems (6 papers)Advanced Neural Network Applications (4 papers)Quantum Computing Algorithms and Architecture (4 papers)
- Partner nations
- ChinaUnited StatesAustralia
In The Last Decade
Song Cheng
23 papers receiving 447 citations
Peers
Comparison fields: 5 of 62
- Atomic and Molecular Physics, and Optics 282
- Artificial Intelligence 265
- Statistical and Nonlinear Physics 75
- Computational Mathematics 75
- Condensed Matter Physics 74
Countries citing papers authored by Song Cheng
This map shows the geographic impact of Song Cheng'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 Song Cheng with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Song Cheng more than expected).
Fields of papers citing papers by Song Cheng
This network shows the impact of papers produced by Song Cheng. 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 Song Cheng. The network helps show where Song Cheng may publish in the future.
Co-authorship network of co-authors of Song Cheng
This figure shows the co-authorship network connecting the top 25 collaborators of Song Cheng. A scholar is included among the top collaborators of Song Cheng 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 Song Cheng. Song Cheng is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 13 | |
| 2 | 14 | |
| 3 | 6 | |
| 4 | 39 | |
| 5 | 1 | |
| 6 | 1 | |
| 7 | 4 | |
| 8 | 70 | |
| 9 | 62 | |
| 10 | 148 | |
| 11 | 8 | |
| 12 | 5 | |
| 13 | 4 | |
| 14 | 1 | |
| 15 | 30 | |
| 16 | 1 | |
| 17 | Real-time motorway traffic state estimation based on unscented Kalman filtering | 1 |
| 18 | 6 | |
| 19 | Algorithm for geometric distorted image rectification based on neural network | 1 |
| 20 | Novel algorithm for image segmentation based on fuzzy set theory | 0 |
About Song Cheng
Song Cheng is a scholar working on Computational Mathematics, Computer Vision and Pattern Recognition and Artificial Intelligence, having authored 26 papers that have together received 468 indexed citations. Recurring topics across this work include Quantum many-body systems (6 papers), Advanced Neural Network Applications (4 papers) and Quantum Computing Algorithms and Architecture (4 papers). The work is most often cited by research in Computational Mathematics (75 citations), Atomic and Molecular Physics, and Optics (282 citations) and Artificial Intelligence (265 citations). Song Cheng has collaborated with scholars based in China, United States and Australia. Frequent co-authors include Lei Wang, Jing Chen, Tao Xiang, Haidong Xie, Lei Wang, Pan Zhang, Pengxiang Xu, Xi‐Wen Guan, Yongxiang Liu and Bei Zeng. Their work appears in journals such as Physical Review Letters, Nuclear Physics B and Physics in Medicine and Biology.
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.