Arvind Mohan
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- Model Reduction and Neural Networks 13
- Computational Mechanics top 5%
- Fluid Dynamics and Turbulent Flows 14
- Fluid Dynamics and Vibration Analysis 8
- Nuclear and High Energy Physics top 10%
- Nuclear physics research studies 5
- Radiation top 10%
- Nuclear Physics and Applications 4
- Aerospace Engineering top 10%
- Aerodynamics and Acoustics in Jet Flows 5
- Nuclear reactor physics and engineering 3
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- Meteorological Phenomena and Simulations 3
- Co-authors
- Datta V. GaitondeDaniel LivescuA. E. LovellMichael ChertkovMiguel R. VisbalMatthew R. MumpowerNicholas LubbersPrasanna Balaprakash
- Partner nations
- United StatesIndiaUnited Kingdom
In The Last Decade
Arvind Mohan
32 papers receiving 442 citations
Peers
Comparison fields: 5 of 74
- Statistical and Nonlinear Physics 170
- Computational Mechanics 196
- Nuclear and High Energy Physics 92
- Radiation 61
- Aerospace Engineering 138
Countries citing papers authored by Arvind Mohan
This map shows the geographic impact of Arvind Mohan'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 Arvind Mohan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Arvind Mohan more than expected).
Fields of papers citing papers by Arvind Mohan
This network shows the impact of papers produced by Arvind Mohan. 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 Arvind Mohan. The network helps show where Arvind Mohan may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Arvind Mohan, 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 | 2025 | 0 | |
| 2 | 2025 | 2 | |
| 3 | 2024 | 2 | |
| 4 | 2023 | 28 | |
| 5 | 2023 | 5 | |
| 6 | 2023 | 25 | |
| 7 | 2023 | 1 | |
| 8 | 2023 | 5 | |
| 9 | 2023 | 2 | |
| 10 | 2023 | 2 | |
| 11 | 2022 | 5 | |
| 12 | 2022 | 38 | |
| 13 | Embedding Hard Physical Constraints in Convolutional Neural Networks for 3D Turbulence | 2020 | 11 |
| 14 | Rapid Spatiotemporal Turbulence Modeling with Convolutional Neural ODEs | 2020 | 1 |
| 15 | Wavelet-Powered Neural Networks for Turbulence | 2020 | 3 |
| 16 | 2020 | 29 | |
| 17 | 2020 | 78 | |
| 18 | 2018 | 3 | |
| 19 | 2017 | 28 | |
| 20 | 2015 | 4 |
About Arvind Mohan
Arvind Mohan is a scholar working on Statistical and Nonlinear Physics, Computational Mechanics, Radiation, Nuclear and High Energy Physics and Aerospace Engineering, having authored 34 papers that have together received 458 indexed citations. Recurring topics across this work include Fluid Dynamics and Turbulent Flows (14 papers), Model Reduction and Neural Networks (13 papers), Fluid Dynamics and Vibration Analysis (8 papers), Nuclear physics research studies (5 papers), Aerodynamics and Acoustics in Jet Flows (5 papers), Nuclear Physics and Applications (4 papers), Nuclear reactor physics and engineering (3 papers) and Meteorological Phenomena and Simulations (3 papers). The work is most often cited by research in Statistical and Nonlinear Physics (170 citations), Computational Mechanics (196 citations), Nuclear and High Energy Physics (92 citations), Radiation (61 citations) and Aerospace Engineering (138 citations). Arvind Mohan has collaborated with scholars based in United States, India and United Kingdom. Frequent co-authors include Datta V. Gaitonde, Daniel Livescu, A. E. Lovell, Michael Chertkov, Miguel R. Visbal, Matthew R. Mumpower, Nicholas Lubbers, Prasanna Balaprakash, Romit Maulik and Sandeep Madireddy. Their work appears in journals such as Physical review. C, Computers & Fluids, Nature Communications, Nature Machine Intelligence and Physical Review Fluids.
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.