J. Nathan Kutz
- Statistical and Nonlinear Physics top 0.01%
- Computational Mechanics top 0.1%
- Atomic and Molecular Physics, and Optics top 0.5%
- Electrical and Electronic Engineering top 1%
- Control and Systems Engineering top 0.2%
- Co-authors
- Steven L. BruntonJoshua L. ProctorBingni W. BruntonSamuel RudyBethany LuschJared C. BronskiBernard DeconinckLincoln D. Carr
- Topics
- Advanced Fiber Laser Technologies (116 papers)Model Reduction and Neural Networks (85 papers)Laser-Matter Interactions and Applications (58 papers)
- Cited by
- Statistical and Nonlinear PhysicsStatistics, Probability and UncertaintyComputational Mechanics
- Partner nations
- United StatesUnited KingdomHong Kong
In The Last Decade
J. Nathan Kutz
329 papers receiving 17.6k citations
Hit Papers
Peers
Comparison fields: 5 of 196
- Statistical and Nonlinear Physics 9.2k
- Computational Mechanics 3.9k
- Atomic and Molecular Physics, and Optics 3.4k
- Electrical and Electronic Engineering 3.2k
- Control and Systems Engineering 2.8k
Countries citing papers authored by J. Nathan Kutz
This map shows the geographic impact of J. Nathan Kutz'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 J. Nathan Kutz with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites J. Nathan Kutz more than expected).
Fields of papers citing papers by J. Nathan Kutz
This network shows the impact of papers produced by J. Nathan Kutz. 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 J. Nathan Kutz. The network helps show where J. Nathan Kutz may publish in the future.
Co-authorship network of co-authors of J. Nathan Kutz
This figure shows the co-authorship network connecting the top 25 collaborators of J. Nathan Kutz. A scholar is included among the top collaborators of J. Nathan Kutz 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 J. Nathan Kutz. J. Nathan Kutz 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 | 7 | |
| 3 | 3 | |
| 4 | 2 | |
| 5 | 3 | |
| 6 | 1 | |
| 7 | 7 | |
| 8 | 17 | |
| 9 | 6 | |
| 10 | 10 | |
| 11 | 14 | |
| 12 | 14 | |
| 13 | 11 | |
| 14 | 3 | |
| 15 | 9 | |
| 16 | 190 | |
| 17 | 149 | |
| 18 | 28 | |
| 19 | 59 | |
| 20 | Multi-Resolution Analysis of Dynamical Systems using Dynamic Mode Decomposition | 2 |
About J. Nathan Kutz
J. Nathan Kutz is a scholar working on Statistical and Nonlinear Physics, Computational Mathematics and Atomic and Molecular Physics, and Optics, having authored 341 papers that have together received 18.2k indexed citations. Recurring topics across this work include Advanced Fiber Laser Technologies (116 papers), Model Reduction and Neural Networks (85 papers) and Laser-Matter Interactions and Applications (58 papers). The work is most often cited by research in Statistical and Nonlinear Physics (9.2k citations), Statistics, Probability and Uncertainty (2.4k citations) and Computational Mechanics (3.9k citations). J. Nathan Kutz has collaborated with scholars based in United States, United Kingdom and Hong Kong. Frequent co-authors include Steven L. Brunton, Joshua L. Proctor, Bingni W. Brunton, Samuel Rudy, Bethany Lusch, Jared C. Bronski, Bernard Deconinck, Lincoln D. Carr, Eurika Kaiser and Xing Fu. Their work appears in journals such as Nature, Science and Proceedings of the National Academy of Sciences.
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.