Ashish Rayal
Impact in
- Modeling and Simulation top 5%
- Fractional Differential Equations Solutions
- Numerical Analysis top 10%
- Differential Equations and Numerical Methods
- Iterative Methods for Nonlinear Equations
- Numerical methods for differential equations
Papers in
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- Fractional Differential Equations Solutions 12
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- Differential Equations and Numerical Methods 7
- Iterative Methods for Nonlinear Equations 3
- Co-authors
- Bhagawati Prasad Joshi (6 shared papers)Abhay Kumar (3 shared papers)Nagendra Kumar (3 shared papers)Akhilesh Kumar Singh (3 shared papers)Akhilesh Kumar Singh (1 shared paper)Madan Mohan Sati (1 shared paper)Navneet Joshi (1 shared paper)Amit Mittal (2 shared papers)
In The Last Decade
Ashish Rayal
18 papers receiving 213 citations
Peers
Comparison fields: 5 of 53
- Modeling and Simulation 97
- Numerical Analysis 59
- Statistical and Nonlinear Physics 36
- Applied Mathematics 27
- General Energy 2
Countries citing papers authored by Ashish Rayal
This map shows the geographic impact of Ashish Rayal'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 Ashish Rayal with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ashish Rayal more than expected).
Fields of papers citing papers by Ashish Rayal
This network shows the impact of papers produced by Ashish Rayal. 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 Ashish Rayal. The network helps show where Ashish Rayal may publish in the future.
Co-authors
The 24 scholars most cited alongside Ashish Rayal, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 24 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2023 | 36 | |
| 2 | 2023 | 35 | |
| 3 | 2020 | 32 | |
| 4 | 2020 | 28 | |
| 5 | 2023 | 20 | |
| 6 | 2023 | 19 | |
| 7 | 2022 | 18 | |
| 8 | 2023 | 11 | |
| 9 | 2020 | 10 | |
| 10 | 2023 | 7 | |
| 11 | 2023 | 6 | |
| 12 | 2024 | 5 | |
| 13 | 2024 | 5 | |
| 14 | 2023 | 4 | |
| 15 | 2024 | 3 | |
| 16 | 2024 | 3 | |
| 17 | 2024 | 2 | |
| 18 | 2025 | 2 | |
| 19 | 2025 | 1 | |
| 20 | 2025 | 1 |
About Ashish Rayal
Ashish Rayal is a scholar working on Modeling and Simulation, Numerical Analysis, Control and Systems Engineering, Statistical and Nonlinear Physics and Computer Vision and Pattern Recognition, having authored 24 papers that have together received 248 indexed citations. Recurring topics across this work include Fractional Differential Equations Solutions (12 papers), Differential Equations and Numerical Methods (7 papers), Nonlinear Waves and Solitons (4 papers), Mathematical functions and polynomials (3 papers), Advanced Control Systems Design (3 papers), Multi-Criteria Decision Making (3 papers), Iterative Methods for Nonlinear Equations (3 papers) and Optimization and Mathematical Programming (2 papers). The work is most often cited by research in Modeling and Simulation (97 citations), Numerical Analysis (59 citations), Statistical and Nonlinear Physics (36 citations), Applied Mathematics (27 citations) and General Energy (2 citations). Ashish Rayal has collaborated with scholars based in India, Italy and Portugal. Frequent co-authors include Bhagawati Prasad Joshi, Abhay Kumar, Nagendra Kumar, Akhilesh Kumar Singh, Akhilesh Kumar Singh, Madan Mohan Sati, Navneet Joshi, Amit Mittal, Shailendra Giri and Akhilesh Singh. Their work appears in journals such as Applied Numerical Mathematics, Computer Modeling in Engineering & Sciences, Chaos Solitons & Fractals, Journal of Vibration and Control and Physica Scripta.
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.