Amit Surana
- Statistical and Nonlinear Physics top 2%
- Building and Construction top 2%
- Computational Mechanics top 5%
- Strategy and Management top 5%
- Electrical and Electronic Engineering
- Co-authors
- Soundar KumaraMark GreavesUsha Nandini RaghavanGeorge HallerSatish NarayananYiqing LinAndrzej BanaszukSean Meyn
- Topics
- Model Reduction and Neural Networks (8 papers)Human-Automation Interaction and Safety (6 papers)Robotic Path Planning Algorithms (6 papers)
- Journals
- Proceedings of the National Academy of SciencesNature CommunicationsJournal of Fluid Mechanics
- Partner nations
- United StatesIrelandFinland
In The Last Decade
Amit Surana
75 papers receiving 1.7k citations
Peers
Comparison fields: 5 of 114
- Statistical and Nonlinear Physics 319
- Building and Construction 302
- Computational Mechanics 283
- Strategy and Management 271
- Electrical and Electronic Engineering 244
Countries citing papers authored by Amit Surana
This map shows the geographic impact of Amit Surana'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 Amit Surana with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Amit Surana more than expected).
Fields of papers citing papers by Amit Surana
This network shows the impact of papers produced by Amit Surana. 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 Amit Surana. The network helps show where Amit Surana may publish in the future.
Co-authorship network of co-authors of Amit Surana
This figure shows the co-authorship network connecting the top 25 collaborators of Amit Surana. A scholar is included among the top collaborators of Amit Surana 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 Amit Surana. Amit Surana 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 | 1 | |
| 3 | 2 | |
| 4 | 4 | |
| 5 | 2 | |
| 6 | 0 | |
| 7 | 3 | |
| 8 | 7 | |
| 9 | 2 | |
| 10 | 26 | |
| 11 | 11 | |
| 12 | 4 | |
| 13 | 4 | |
| 14 | 98 | |
| 15 | 73 | |
| 16 | 2 | |
| 17 | 15 | |
| 18 | 146 | |
| 19 | 31 | |
| 20 | 8 |
About Amit Surana
Amit Surana is a scholar working on Computational Mathematics, Statistical and Nonlinear Physics and Artificial Intelligence, having authored 81 papers that have together received 1.8k indexed citations. Recurring topics across this work include Model Reduction and Neural Networks (8 papers), Human-Automation Interaction and Safety (6 papers) and Robotic Path Planning Algorithms (6 papers). The work is most often cited by research in Statistical and Nonlinear Physics (319 citations), Computational Mathematics (15 citations) and Management Information Systems (206 citations). Amit Surana has collaborated with scholars based in United States, Ireland and Finland. Frequent co-authors include Soundar Kumara, Mark Greaves, Usha Nandini Raghavan, George Haller, Satish Narayanan, Yiqing Lin, Andrzej Banaszuk, Sean Meyn, Ankur Kamthe and Rohini Brahme. Their work appears in journals such as Proceedings of the National Academy of Sciences, Nature Communications and Journal of Fluid Mechanics.
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.