Arvind T. Mohan

29 papers receiving 525 citations

Peers

Arvind T. Mohan
Comparison fields: 5 of 45
  • Statistical and Nonlinear Physics 241
  • Nuclear and High Energy Physics 139
  • Computational Mechanics 72
  • Aerospace Engineering 58
  • Computer Graphics and Computer-Aided Design 9
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Citations per year

Countries citing papers authored by Arvind T. Mohan

Since Specialization
Citations

This map shows the geographic impact of Arvind T. 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 T. 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 T. Mohan more than expected).

Fields of papers citing papers by Arvind T. Mohan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Arvind T. 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 T. Mohan. The network helps show where Arvind T. Mohan may publish in the future.

Co-authors

The 25 scholars most cited alongside Arvind T. Mohan, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Arvind T. Mohan Line = papers co-authored together Arvind T. Mohan links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 29 papers — load more, or switch the sort, to bring in the rest.

#Work
1 202094
2 202356
3 202252
4 202248
5 201646
6 202042
7 202031
8 201728
9 202328
10 202022
11 201516
12
Embedding Hard Physical Constraints in Convolutional Neural Networks for 3D Turbulence
202012
13 20239
14 20197
15 20227
16 20237
17 20154
18
Wavelet-Powered Neural Networks for Turbulence
20204
19 20143
20 20183

About Arvind T. Mohan

Arvind T. Mohan is a scholar working on Statistical and Nonlinear Physics, Nuclear and High Energy Physics, Aerospace Engineering, Computer Graphics and Computer-Aided Design and Computational Mechanics, having authored 29 papers that have together received 539 indexed citations. Recurring topics across this work include Model Reduction and Neural Networks (16 papers), Nuclear physics research studies (6 papers), Plasma and Flow Control in Aerodynamics (2 papers), Computational Fluid Dynamics and Aerodynamics (2 papers), Fluid Dynamics and Turbulent Flows (2 papers), Aerospace and Aviation Technology (1 paper), Aeroelasticity and Vibration Control (1 paper) and Elasticity and Material Modeling (1 paper). The work is most often cited by research in Statistical and Nonlinear Physics (241 citations), Nuclear and High Energy Physics (139 citations), Computational Mechanics (72 citations), Aerospace Engineering (58 citations) and Computer Graphics and Computer-Aided Design (9 citations). Arvind T. Mohan has collaborated with scholars based in United States, India and Canada. Frequent co-authors include Daniel Livescu, Amy Elizabeth Lovell, Datta V. Gaitonde, Matthew Ryan Mumpower, T. M. Sprouse, Miguel R. Visbal, Nicholas Lubbers, Romit Maulik, Sandeep Madireddy and Bethany Lusch. Their work appears in journals such as Physical Review C, International Journal for Numerical Methods in Biomedical Engineering, Computers in Biology and Medicine, Journal of Physics G Nuclear and Particle Physics and Journal of Aircraft.

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.

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