S. S. Ravindran
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- Model Reduction and Neural Networks 16
- Computational Mechanics top 1%
- Advanced Numerical Methods in Computational Mathematics 27
- Computational Fluid Dynamics and Aerodynamics 20
- Fluid Dynamics and Turbulent Flows 11
- Numerical Analysis top 5%
- Numerical methods for differential equations 8
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- Advanced Mathematical Modeling in Engineering 10
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- Stability and Controllability of Differential Equations 9
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- Navier-Stokes equation solutions 7
- Co-authors
- Kazufumi ItoLisheng HouKikukatsu ItoAlok MajumdarYue YanMax GunzburgerJames C. TurnerParthasarathi Ghosh
- Journals
- International Journal for Numerical Methods in Fluids (5 papers)SIAM Journal on Scientific Computing (4 papers)Numerical Functional Analysis and Optimization (4 papers)
- Partner nations
- United StatesCanadaIndia
In The Last Decade
S. S. Ravindran
50 papers receiving 1.2k citations
Peers
Comparison fields: 5 of 65
- Statistical and Nonlinear Physics 711
- Computational Mechanics 881
- Numerical Analysis 211
- Statistics, Probability and Uncertainty 217
- Computational Theory and Mathematics 181
Countries citing papers authored by S. S. Ravindran
This map shows the geographic impact of S. S. Ravindran'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 S. S. Ravindran with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites S. S. Ravindran more than expected).
Fields of papers citing papers by S. S. Ravindran
This network shows the impact of papers produced by S. S. Ravindran. 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 S. S. Ravindran. The network helps show where S. S. Ravindran may publish in the future.
Co-authorship network
The 8 scholars most cited alongside S. S. Ravindran, 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 | 1 | |
| 2 | 2023 | 1 | |
| 3 | 2022 | 0 | |
| 4 | 2020 | 1 | |
| 5 | 2019 | 0 | |
| 6 | 2016 | 6 | |
| 7 | 2015 | 1 | |
| 8 | 2015 | 1 | |
| 9 | 2015 | 7 | |
| 10 | 2010 | 8 | |
| 11 | 2008 | 14 | |
| 12 | 2007 | 8 | |
| 13 | 2003 | 3 | |
| 14 | 2002 | 2 | |
| 15 | 2002 | 71 | |
| 16 | 2002 | 4 | |
| 17 | 2001 | 49 | |
| 18 | Proper Orthogonal Decomposition in Optimal Control of Fluids | 1999 | 55 |
| 19 | 1998 | 203 | |
| 20 | 1997 | 15 |
About S. S. Ravindran
S. S. Ravindran is a scholar working on Numerical Analysis, Computational Mechanics and Statistical and Nonlinear Physics, having authored 54 papers that have together received 1.3k indexed citations. Recurring topics across this work include Advanced Numerical Methods in Computational Mathematics (27 papers), Computational Fluid Dynamics and Aerodynamics (20 papers), Model Reduction and Neural Networks (16 papers), Fluid Dynamics and Turbulent Flows (11 papers), Advanced Mathematical Modeling in Engineering (10 papers), Stability and Controllability of Differential Equations (9 papers), Numerical methods for differential equations (8 papers) and Navier-Stokes equation solutions (7 papers). The work is most often cited by research in Statistical and Nonlinear Physics (711 citations), Computational Mechanics (881 citations) and Numerical Analysis (211 citations). S. S. Ravindran has collaborated with scholars based in United States, Canada and India. Frequent co-authors include Kazufumi Ito, Lisheng Hou, Kikukatsu Ito, Alok Majumdar, Yue Yan, Max Gunzburger, James C. Turner and Parthasarathi Ghosh. Their work appears in journals such as International Journal for Numerical Methods in Fluids, SIAM Journal on Scientific Computing, Numerical Functional Analysis and Optimization, Computer Methods in Applied Mechanics and Engineering and Numerical Methods for Partial Differential Equations.
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.