Chai Wah Wu
- Computer Networks and Communications top 0.1%
- Statistical and Nonlinear Physics top 0.05%
- Computer Vision and Pattern Recognition top 1%
- Artificial Intelligence top 2%
- Molecular Biology
- Co-authors
- Leon O. ChuaTao YangGuo‐Qun ZhongT. RoskaMakoto ItohK. S. HALLEJune H. LarrabeeLin-Bao Yang
- Topics
- Nonlinear Dynamics and Pattern Formation (53 papers)Neural Networks Stability and Synchronization (52 papers)Chaos control and synchronization (31 papers)
- Cited by
- Statistical and Nonlinear PhysicsComputer Networks and CommunicationsComputer Vision and Pattern Recognition
- Journals
- Proceedings of the National Academy of SciencesIEEE Transactions on Automatic ControlPhysics Letters A
- Partner nations
- United StatesChinaRussia
In The Last Decade
Chai Wah Wu
156 papers receiving 6.8k citations
Hit Papers
Peers
Comparison fields: 5 of 130
- Computer Networks and Communications 5.4k
- Statistical and Nonlinear Physics 3.8k
- Computer Vision and Pattern Recognition 880
- Artificial Intelligence 829
- Molecular Biology 593
Countries citing papers authored by Chai Wah Wu
This map shows the geographic impact of Chai Wah Wu'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 Chai Wah Wu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Chai Wah Wu more than expected).
Fields of papers citing papers by Chai Wah Wu
This network shows the impact of papers produced by Chai Wah Wu. 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 Chai Wah Wu. The network helps show where Chai Wah Wu may publish in the future.
Co-authorship network of co-authors of Chai Wah Wu
This figure shows the co-authorship network connecting the top 25 collaborators of Chai Wah Wu. A scholar is included among the top collaborators of Chai Wah Wu 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 Chai Wah Wu. Chai Wah Wu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 4 | |
| 3 | 6 | |
| 4 | 12 | |
| 5 | 8 | |
| 6 | 2 | |
| 7 | A Family of Robust Stochastic Operators for Reinforcement Learning | 1 |
| 8 | A Control-Model-Based Approach for Reinforcement Learning. | 2 |
| 9 | 3 | |
| 10 | 1 | |
| 11 | 5 | |
| 12 | 12 | |
| 13 | 2 | |
| 14 | 2 | |
| 15 | 175 | |
| 16 | 46 | |
| 17 | 40 | |
| 18 | 4 | |
| 19 | 14 | |
| 20 | 19 |
About Chai Wah Wu
Chai Wah Wu is a scholar working on Statistical and Nonlinear Physics, Computer Networks and Communications and Computational Theory and Mathematics, having authored 162 papers that have together received 7.1k indexed citations. Recurring topics across this work include Nonlinear Dynamics and Pattern Formation (53 papers), Neural Networks Stability and Synchronization (52 papers) and Chaos control and synchronization (31 papers). The work is most often cited by research in Statistical and Nonlinear Physics (3.8k citations), Computer Networks and Communications (5.4k citations) and Computer Vision and Pattern Recognition (880 citations). Chai Wah Wu has collaborated with scholars based in United States, China and Russia. Frequent co-authors include Leon O. Chua, Tao Yang, Guo‐Qun Zhong, T. Roska, Tao Yang, Makoto Itoh, K. S. HALLE, June H. Larrabee, Lin-Bao Yang and Marco Balsi. Their work appears in journals such as Proceedings of the National Academy of Sciences, IEEE Transactions on Automatic Control and Physics Letters A.
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.