Gaowen Liu
- Computer Vision and Pattern Recognition top 1%
- Mechanical Engineering top 5%
- Aerospace Engineering top 5%
- Computational Mechanics top 5%
- Artificial Intelligence top 5%
- Topics
- Turbomachinery Performance and Optimization (27 papers)Heat Transfer Mechanisms (26 papers)Tribology and Lubrication Engineering (11 papers)
- Partner nations
- ChinaUnited StatesItaly
In The Last Decade
Gaowen Liu
76 papers receiving 1.3k citations
Peers
Comparison fields: 5 of 105
- Computer Vision and Pattern Recognition 675
- Mechanical Engineering 399
- Aerospace Engineering 393
- Computational Mechanics 286
- Artificial Intelligence 207
Countries citing papers authored by Gaowen Liu
This map shows the geographic impact of Gaowen Liu'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 Gaowen Liu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Gaowen Liu more than expected).
Fields of papers citing papers by Gaowen Liu
This network shows the impact of papers produced by Gaowen Liu. 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 Gaowen Liu. The network helps show where Gaowen Liu may publish in the future.
Co-authorship network of co-authors of Gaowen Liu
This figure shows the co-authorship network connecting the top 25 collaborators of Gaowen Liu. A scholar is included among the top collaborators of Gaowen Liu 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 Gaowen Liu. Gaowen Liu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 2 | |
| 2 | 1 | |
| 3 | 0 | |
| 4 | 1 | |
| 5 | 2 | |
| 6 | 2 | |
| 7 | 0 | |
| 8 | 3 | |
| 9 | 7 | |
| 10 | 7 | |
| 11 | 0 | |
| 12 | 5 | |
| 13 | 1 | |
| 14 | 2 | |
| 15 | 13 | |
| 16 | 11 | |
| 17 | Inferring painting style with multi-task dictionary learning | 19 |
| 18 | Experiment of the flow in a cover-plate pre-swirl system | 1 |
| 19 | Factors influencing regenerative braking of parallel hydraulic hybrid vehicles | 3 |
| 20 | Numerical simulation on heat transfer and pressure drop in pin-fin trapezoidal and rectangular duct | 0 |
About Gaowen Liu
Gaowen Liu is a scholar working on Aerospace Engineering, Computational Mechanics and Computer Vision and Pattern Recognition, having authored 84 papers that have together received 1.3k indexed citations. Recurring topics across this work include Turbomachinery Performance and Optimization (27 papers), Heat Transfer Mechanisms (26 papers) and Tribology and Lubrication Engineering (11 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (675 citations), Aerospace Engineering (393 citations) and Computational Mechanics (286 citations). Gaowen Liu has collaborated with scholars based in China, United States and Italy. Frequent co-authors include Nicu Sebe, Yan Yan, Elisa Ricci, Aqiang Lin, Ramanathan Subramanian, Qing Feng, Yan Yan, Yuxin Liu, Yi Yang and Deyu Meng. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Nature Methods and Applied Energy.
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.