V. Govindaraj

1.6k total citations
97 papers, 1.2k citations indexed

About

V. Govindaraj is a scholar working on Modeling and Simulation, Applied Mathematics and Numerical Analysis. According to data from OpenAlex, V. Govindaraj has authored 97 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 81 papers in Modeling and Simulation, 46 papers in Applied Mathematics and 30 papers in Numerical Analysis. Recurrent topics in V. Govindaraj's work include Fractional Differential Equations Solutions (80 papers), Nonlinear Differential Equations Analysis (45 papers) and Advanced Control Systems Design (21 papers). V. Govindaraj is often cited by papers focused on Fractional Differential Equations Solutions (80 papers), Nonlinear Differential Equations Analysis (45 papers) and Advanced Control Systems Design (21 papers). V. Govindaraj collaborates with scholars based in India, Türkiye and Saudi Arabia. V. Govindaraj's co-authors include Pushpendra Kumar, Vedat Suat Ertürk, K. Balachandran, Juan J. Trujillo, Raju K. George, L. Rodríguez-Germá, M. Rivero, Zaid Odibat, Wedad Albalawi and Dumitru Bǎleanu and has published in prestigious journals such as SHILAP Revista de lepidopterología, Expert Systems with Applications and Cardiovascular Research.

In The Last Decade

V. Govindaraj

86 papers receiving 1.1k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
V. Govindaraj India 20 878 422 260 257 206 97 1.2k
Samaneh Sadat Sajjadi Iran 14 894 1.0× 290 0.7× 309 1.2× 168 0.7× 305 1.5× 19 1.3k
Ndolane Sene Senegal 27 1.4k 1.6× 391 0.9× 439 1.7× 221 0.9× 401 1.9× 74 1.9k
Osman Tunç Türkiye 21 656 0.7× 585 1.4× 451 1.7× 211 0.8× 219 1.1× 71 1.1k
Samir Hadid United Arab Emirates 17 830 0.9× 336 0.8× 296 1.1× 129 0.5× 186 0.9× 46 1.1k
Asifa Tassaddiq Saudi Arabia 29 775 0.9× 320 0.8× 281 1.1× 98 0.4× 144 0.7× 116 2.5k
Salah Boulaaras Saudi Arabia 25 824 0.9× 565 1.3× 339 1.3× 641 2.5× 345 1.7× 307 2.2k
S. Z. Rida Egypt 22 840 1.0× 268 0.6× 380 1.5× 81 0.3× 285 1.4× 72 1.2k
Necati Özdemir Türkiye 26 1.6k 1.8× 493 1.2× 573 2.2× 253 1.0× 494 2.4× 70 1.9k
Mati ur Rahman China 26 1.2k 1.3× 372 0.9× 211 0.8× 154 0.6× 554 2.7× 126 1.9k
Hemen Dutta India 21 525 0.6× 427 1.0× 335 1.3× 141 0.5× 223 1.1× 139 1.4k

Countries citing papers authored by V. Govindaraj

Since Specialization
Citations

This map shows the geographic impact of V. Govindaraj'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 V. Govindaraj with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites V. Govindaraj more than expected).

Fields of papers citing papers by V. Govindaraj

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by V. Govindaraj. 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 V. Govindaraj. The network helps show where V. Govindaraj may publish in the future.

Co-authorship network of co-authors of V. Govindaraj

This figure shows the co-authorship network connecting the top 25 collaborators of V. Govindaraj. A scholar is included among the top collaborators of V. Govindaraj based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with V. Govindaraj. V. Govindaraj is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Govindaraj, V., et al.. (2025). An improved physics informed neural network with theory of functional connections for fractional differential equations. Engineering Analysis with Boundary Elements. 178. 106281–106281.
2.
3.
Kumar, Pushpendra, et al.. (2024). A Chebyshev neural network-based numerical scheme to solve distributed-order fractional differential equations. Computers & Mathematics with Applications. 164. 150–165. 17 indexed citations
4.
Govindaraj, V., et al.. (2024). Examining reachability criteria for fractional dynamical systems with mixed delays in control utilizingψ-Hilfer pseudo-fractional derivative. Chaos Solitons & Fractals. 181. 114702–114702. 1 indexed citations
5.
Govindaraj, V., et al.. (2024). Investigation of controllability and stability of fractional dynamical systems with delay in control. Mathematics and Computers in Simulation. 220. 89–104. 6 indexed citations
6.
Govindaraj, V., et al.. (2024). Controllability of the generalised proportional fractional dynamical systems having delays in control. Journal of Control and Decision. 1–16.
7.
Govindaraj, V., et al.. (2024). Controllability of time-varying fractional dynamical systems with distributed delays in control. Physica Scripta. 99(6). 65218–65218. 2 indexed citations
8.
Samei, Mohammad Esmael, et al.. (2024). Study of three-point impulsive boundary value problems governed by $$\Psi $$-Caputo fractional derivative. Journal of Applied Mathematics and Computing. 70(4). 3947–3983. 4 indexed citations
9.
Govindaraj, V., et al.. (2024). Examining reachability of fractional dynamical systems with delays in control utilizing ψ-Hilfer pseudo-fractional derivative. Physica Scripta. 99(3). 35225–35225. 1 indexed citations
10.
Govindaraj, V., et al.. (2024). Controllability Results for $$\psi $$-Caputo Fractional Differential Systems with Impulsive Effects. Qualitative Theory of Dynamical Systems. 23(4). 4 indexed citations
11.
Kumar, Pushpendra, et al.. (2023). A neural networks-based numerical method for the generalized Caputo-type fractional differential equations. Mathematics and Computers in Simulation. 213. 302–323. 34 indexed citations
12.
Govindaraj, V., et al.. (2023). A novel numerical approach for time-varying impulsive fractional differential equations using theory of functional connections and neural network. Expert Systems with Applications. 238. 121750–121750. 29 indexed citations
13.
Kumar, Pushpendra, et al.. (2023). Forecasting of HIV/AIDS in South Africa using 1990 to 2021 data: novel integer- and fractional-order fittings. International Journal of Dynamics and Control. 12(7). 2247–2263. 15 indexed citations
14.
Govindaraj, V., et al.. (2023). Controllability of fractional dynamical systems with distributed delays in control using ψ‐Caputo fractional derivative. Asian Journal of Control. 25(6). 4257–4267. 7 indexed citations
15.
Kumar, Pushpendra, et al.. (2023). A novel numerical scheme for fractional differential equations using extreme learning machine. Physica A Statistical Mechanics and its Applications. 622. 128887–128887. 20 indexed citations
16.
Khater, Mostafa M. A. & V. Govindaraj. (2023). Effectives of Different Shaped Dimples on a NACA Airfoil. 2023. 29–37. 10 indexed citations
17.
18.
Kumar, Pushpendra, Vedat Suat Ertürk, V. Govindaraj, Hamadjam Abboubakar, & Kottakkaran Sooppy Nisar. (2022). Dynamics of COVID-19 epidemic via two different fractional derivatives. Advances in Complex Systems. 14(3). 4 indexed citations
19.
Sousa, J. Vanterler da C., et al.. (2021). Reachability of fractional dynamical systems using ψ-Hilfer pseudo-fractional derivative. Journal of Mathematical Physics. 62(8). 17 indexed citations
20.
Odibat, Zaid, Vedat Suat Ertürk, Pushpendra Kumar, & V. Govindaraj. (2021). Dynamics of generalized Caputo type delay fractional differential equations using a modified Predictor-Corrector scheme. Physica Scripta. 96(12). 125213–125213. 45 indexed citations

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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