Manoj Kumar

2.3k total citations
105 papers, 1.8k citations indexed

About

Manoj Kumar is a scholar working on Numerical Analysis, Modeling and Simulation and Applied Mathematics. According to data from OpenAlex, Manoj Kumar has authored 105 papers receiving a total of 1.8k indexed citations (citations by other indexed papers that have themselves been cited), including 80 papers in Numerical Analysis, 65 papers in Modeling and Simulation and 32 papers in Applied Mathematics. Recurrent topics in Manoj Kumar's work include Fractional Differential Equations Solutions (65 papers), Differential Equations and Numerical Methods (52 papers) and Numerical methods for differential equations (38 papers). Manoj Kumar is often cited by papers focused on Fractional Differential Equations Solutions (65 papers), Differential Equations and Numerical Methods (52 papers) and Numerical methods for differential equations (38 papers). Manoj Kumar collaborates with scholars based in India, Romania and Ethiopia. Manoj Kumar's co-authors include Neha Yadav, Pratibha Verma, Amit Prakash, Sapna Pandit, Anupam Yadav, Hradyesh Kumar Mishra, Pitam Singh, Kapil K. Sharma, Dumitru Bǎleanu and Tariq Aziz and has published in prestigious journals such as SHILAP Revista de lepidopterología, Computer Physics Communications and Chaos Solitons & Fractals.

In The Last Decade

Manoj Kumar

104 papers receiving 1.6k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Manoj Kumar India 21 1.0k 901 495 286 217 105 1.8k
Muhammed I. Syam United Arab Emirates 27 1.4k 1.4× 842 0.9× 659 1.3× 467 1.6× 156 0.7× 133 2.3k
Zhongqiang Zhang United States 22 734 0.7× 687 0.8× 597 1.2× 190 0.7× 269 1.2× 66 1.7k
M.M. Hosseini Iran 20 768 0.8× 665 0.7× 278 0.6× 107 0.4× 202 0.9× 67 1.2k
Subir Das India 29 1.4k 1.4× 797 0.9× 1.3k 2.6× 343 1.2× 656 3.0× 202 3.3k
Kourosh Parand Iran 30 2.2k 2.2× 1.7k 1.9× 947 1.9× 339 1.2× 564 2.6× 187 3.1k
A. Golbabai Iran 28 1.4k 1.4× 1.1k 1.2× 427 0.9× 262 0.9× 475 2.2× 78 1.9k
Fazhan Geng China 25 1.2k 1.2× 1.2k 1.3× 179 0.4× 485 1.7× 518 2.4× 69 1.8k
S. A. Yousefi Iran 28 2.0k 2.0× 1.6k 1.8× 421 0.9× 649 2.3× 453 2.1× 59 2.6k
Chun‐Hui Hsiao Taiwan 15 780 0.8× 570 0.6× 254 0.5× 336 1.2× 230 1.1× 26 1.4k

Countries citing papers authored by Manoj Kumar

Since Specialization
Citations

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

Fields of papers citing papers by Manoj Kumar

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Manoj Kumar

This figure shows the co-authorship network connecting the top 25 collaborators of Manoj Kumar. A scholar is included among the top collaborators of Manoj Kumar 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 Manoj Kumar. Manoj Kumar 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.
Kumar, Manoj, et al.. (2024). Higher order numerical methods for fractional delay differential equations. Indian Journal of Pure and Applied Mathematics. 1 indexed citations
2.
Kumar, Manoj. (2023). An iterative approach for solving fractional order Cauchy reaction-diffusion equations. SHILAP Revista de lepidopterología. 22(3). 19–32. 2 indexed citations
3.
Vivek, Vivek, et al.. (2023). A fast Fibonacci wavelet-based numerical algorithm for the solution of HIV-infected $$\textrm{CD4}^{+}\,\textrm{T}$$ cells model. The European Physical Journal Plus. 138(5). 458–458. 12 indexed citations
4.
Kumar, Manoj. (2022). An Efficient Numerical Scheme for Solving a Fractional-Order System of Delay Differential Equations. International Journal of Applied and Computational Mathematics. 8(5). 262–262. 5 indexed citations
5.
Verma, Pratibha & Manoj Kumar. (2021). On the existence and stability of fuzzy CF variable fractional differential equation for COVID-19 epidemic. Engineering With Computers. 38(S2). 1053–1064. 12 indexed citations
6.
Verma, Pratibha & Manoj Kumar. (2021). NEW EXISTENCE, UNIQUENESS RESULTS FOR MULTI-DIMENSIONAL MULTI-TERM CAPUTO TIME-FRACTIONAL MIXED SUB-DIFFUSION AND DIFFUSION-WAVE EQUATION ON CONVEX DOMAINS. Journal of Applied Analysis & Computation. 11(3). 1455–1480. 8 indexed citations
7.
Verma, Pratibha & Manoj Kumar. (2020). Analysis of a novel coronavirus (2019-nCOV) system with variable Caputo-Fabrizio fractional order. Chaos Solitons & Fractals. 142. 110451–110451. 40 indexed citations
8.
Kumar, Manoj & Varsha Daftardar‐Gejji. (2019). A new family of predictor-corrector methods for solving fractional differential equations. Applied Mathematics and Computation. 363. 124633–124633. 13 indexed citations
9.
Kumar, Manoj, et al.. (2019). A New Trigonometrical Algorithm for Computing Real Root of Non-linear Transcendental Equations. International Journal of Applied and Computational Mathematics. 5(2). 10 indexed citations
10.
Prakash, Amit & Manoj Kumar. (2016). HE'S VARIATIONAL ITERATION METHOD FOR THE SOLUTION OF NONLINEAR NEWELL-WHITEHEAD-SEGEL EQUATION. Journal of Applied Analysis & Computation. 6(3). 738–748. 30 indexed citations
11.
Kumar, Manoj & Neha Yadav. (2015). Numerical Solution of Bratu’s Problem Using Multilayer Perceptron Neural Network Method. National Academy Science Letters. 38(5). 425–428. 26 indexed citations
12.
Kumar, Manoj. (2014). On an new Open type variant of Newton´s method. 10(2). 21–31. 2 indexed citations
13.
Kumar, Manoj, et al.. (2012). Adomian Decomposition Method for Solving Higher Order Boundary Value Problems. Mathematical theory and modeling. 2(1). 11–22. 8 indexed citations
14.
Kumar, Manoj, et al.. (2012). Phase plane analysis and traveling wave solution of third order nonlinear singular problems arising in thin film evolution. Computers & Mathematics with Applications. 64(9). 2886–2895. 4 indexed citations
15.
Kumar, Manoj, et al.. (2011). Methods for solving singular perturbation problems arising in science and engineering. Mathematical and Computer Modelling. 54(1-2). 556–575. 39 indexed citations
16.
Kumar, Manoj & Neha Yadav. (2011). Multilayer perceptrons and radial basis function neural network methods for the solution of differential equations: A survey. Computers & Mathematics with Applications. 62(10). 3796–3811. 131 indexed citations
17.
Kumar, Manoj, Hradyesh Kumar Mishra, & Pitam Singh. (2008). Numerical treatment of singularly perturbed two point boundary value problems using initial-value method. Journal of Applied Mathematics and Computing. 29(1-2). 229–246. 11 indexed citations
18.
Kumar, Manoj. (2003). A difference method for singular two-point boundary value problems. Applied Mathematics and Computation. 146(2-3). 879–884. 14 indexed citations
19.
Kumar, Manoj. (2002). A three-point finite difference method for a class of singular two-point boundary value problems. Journal of Computational and Applied Mathematics. 145(1). 89–97. 29 indexed citations
20.
Aziz, Tariq & Manoj Kumar. (2001). A fourth-order finite-difference method based on non-uniform mesh for a class of singular two-point boundary value problems. Journal of Computational and Applied Mathematics. 136(1-2). 337–342. 16 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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