Kumi Nakai
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- Model Reduction and Neural Networks 8
- Computational Mechanics top 10%
- Sparse and Compressive Sensing Techniques 6
- Fluid Dynamics and Turbulent Flows 6
- Aerospace Engineering top 10%
- Plasma and Flow Control in Aerodynamics 13
- Aerodynamics and Fluid Dynamics Research 9
- Artificial Intelligence top 10%
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- Distributed Sensor Networks and Detection Algorithms 6
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- Aerosol Filtration and Electrostatic Precipitation 6
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- Advanced Multi-Objective Optimization Algorithms 4
- Co-authors
- Taku NonomuraYuji SaitoTakayuki NagataKeigo YamadaHiroyuki NishidaKeisuke AsaiYasuo SasakiShunsuke Ono
- Cited by
- Statistics, Probability and UncertaintyStatistical and Nonlinear PhysicsComputational Mechanics
- Journals
- IEEE Access (4 papers)IEEE Sensors Journal (3 papers)Geophysical Journal International (2 papers)
- Partner nations
- JapanUnited States
In The Last Decade
Kumi Nakai
32 papers receiving 359 citations
Peers
Comparison fields: 5 of 44
- Statistics, Probability and Uncertainty 57
- Statistical and Nonlinear Physics 79
- Computational Mechanics 110
- Aerospace Engineering 101
- Artificial Intelligence 101
Countries citing papers authored by Kumi Nakai
This map shows the geographic impact of Kumi Nakai'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 Kumi Nakai with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kumi Nakai more than expected).
Fields of papers citing papers by Kumi Nakai
This network shows the impact of papers produced by Kumi Nakai. 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 Kumi Nakai. The network helps show where Kumi Nakai may publish in the future.
Co-authorship network
The 20 scholars most cited alongside Kumi Nakai, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2024 | 1 | |
| 2 | 2023 | 7 | |
| 3 | 2023 | 1 | |
| 4 | 2023 | 4 | |
| 5 | 2023 | 5 | |
| 6 | 2022 | 2 | |
| 7 | 2022 | 7 | |
| 8 | 2022 | 1 | |
| 9 | 2022 | 16 | |
| 10 | 2021 | 23 | |
| 11 | 2021 | 9 | |
| 12 | 2021 | 29 | |
| 13 | 2021 | 1 | |
| 14 | 2021 | 14 | |
| 15 | 2020 | 0 | |
| 16 | 2019 | 2 | |
| 17 | 2019 | 1 | |
| 18 | 2018 | 0 | |
| 19 | 2018 | 3 | |
| 20 | 2017 | 30 |
About Kumi Nakai
Kumi Nakai is a scholar working on Statistical and Nonlinear Physics, Aerospace Engineering and Computational Mechanics, having authored 35 papers that have together received 382 indexed citations. Recurring topics across this work include Plasma and Flow Control in Aerodynamics (13 papers), Aerodynamics and Fluid Dynamics Research (9 papers), Model Reduction and Neural Networks (8 papers), Sparse and Compressive Sensing Techniques (6 papers), Fluid Dynamics and Turbulent Flows (6 papers), Distributed Sensor Networks and Detection Algorithms (6 papers), Aerosol Filtration and Electrostatic Precipitation (6 papers) and Advanced Multi-Objective Optimization Algorithms (4 papers). The work is most often cited by research in Statistics, Probability and Uncertainty (57 citations), Statistical and Nonlinear Physics (79 citations) and Computational Mechanics (110 citations). Kumi Nakai has collaborated with scholars based in Japan and United States. Frequent co-authors include Taku Nonomura, Yuji Saito, Takayuki Nagata, Keigo Yamada, Hiroyuki Nishida, Keisuke Asai, Yasuo Sasaki, Shunsuke Ono, Takashi Matsuno and Daisuke Tsubakino. Their work appears in journals such as IEEE Access, IEEE Sensors Journal, Geophysical Journal International, AIP Advances and AIAA Journal.
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.