Levon Nurbekyan

821 total citations
23 papers, 365 citations indexed

About

Levon Nurbekyan is a scholar working on Finance, Statistical and Nonlinear Physics and Applied Mathematics. According to data from OpenAlex, Levon Nurbekyan has authored 23 papers receiving a total of 365 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Finance, 7 papers in Statistical and Nonlinear Physics and 6 papers in Applied Mathematics. Recurrent topics in Levon Nurbekyan's work include Stochastic processes and financial applications (12 papers), Model Reduction and Neural Networks (4 papers) and Nonlinear Partial Differential Equations (4 papers). Levon Nurbekyan is often cited by papers focused on Stochastic processes and financial applications (12 papers), Model Reduction and Neural Networks (4 papers) and Nonlinear Partial Differential Equations (4 papers). Levon Nurbekyan collaborates with scholars based in United States, Saudi Arabia and Portugal. Levon Nurbekyan's co-authors include Stanley Osher, Samy Wu Fung, Wuchen Li, Lars Ruthotto, Diogo A. Gomes, Siting Liu, A. T. Lin, João Saúde, Yunan Yang and Yat Tin Chow and has published in prestigious journals such as Scientific Reports, Journal of Computational Physics and SIAM Journal on Numerical Analysis.

In The Last Decade

Levon Nurbekyan

21 papers receiving 348 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Levon Nurbekyan United States	10	129	106	79	51	49	23	365
Samy Wu Fung United States	9	107 0.8×	45 0.4×	78 1.0×	18 0.4×	25 0.5×	20	308
Nigel J. Newton United Kingdom	9	117 0.9×	226 2.1×	92 1.2×	27 0.5×	23 0.5×	23	496
Franklin Mendivil Canada	11	84 0.7×	31 0.3×	41 0.5×	46 0.9×	18 0.4×	58	404
Ali Shakiba Iran	11	46 0.4×	38 0.4×	73 0.9×	28 0.5×	112 2.3×	32	369
Caibin Zeng China	12	195 1.5×	65 0.6×	29 0.4×	73 1.4×	225 4.6×	46	507
John G. O’Hara United Kingdom	12	85 0.7×	199 1.9×	17 0.2×	26 0.5×	55 1.1×	42	495
Bernard Bercu France	15	36 0.3×	300 2.8×	129 1.6×	55 1.1×	16 0.3×	58	664
Pierre-André Zitt France	10	68 0.5×	51 0.5×	54 0.7×	78 1.5×	31 0.6×	23	401
Mohd Salmi Md Noorani Malaysia	15	196 1.5×	13 0.1×	37 0.5×	58 1.1×	73 1.5×	70	720

Countries citing papers authored by Levon Nurbekyan

Since Specialization

Citations

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

Fields of papers citing papers by Levon Nurbekyan

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Levon Nurbekyan

This figure shows the co-authorship network connecting the top 25 collaborators of Levon Nurbekyan. A scholar is included among the top collaborators of Levon Nurbekyan 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 Levon Nurbekyan. Levon Nurbekyan is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Vidal, Alexander, Samy Wu Fung, Stanley Osher, Luis Tenorio, & Levon Nurbekyan. (2025). Kernel Expansions for High-Dimensional Mean-Field Control with Non-local Interactions. 4164–4171.

Nurbekyan, Levon, et al.. (2024). Applications of No-Collision Transportation Maps in Manifold Learning. SIAM Journal on Mathematics of Data Science. 6(1). 97–126. 3 indexed citations

Vidal, Alexander, Samy Wu Fung, Luis Tenorio, Stanley Osher, & Levon Nurbekyan. (2023). Taming hyperparameter tuning in continuous normalizing flows using the JKO scheme. Scientific Reports. 13(1). 4501–4501. 5 indexed citations

Nurbekyan, Levon, et al.. (2023). Efficient Natural Gradient Descent Methods for Large-Scale PDE-Based Optimization Problems. SIAM Journal on Scientific Computing. 45(4). A1621–A1655. 11 indexed citations

Yang, Yunan, et al.. (2023). Optimal Transport for Parameter Identification of Chaotic Dynamics via Invariant Measures. SIAM Journal on Applied Dynamical Systems. 22(1). 269–310. 7 indexed citations

Nurbekyan, Levon, et al.. (2022). A Neural Network Approach for High-Dimensional Optimal Control Applied to Multiagent Path Finding. IEEE Transactions on Control Systems Technology. 31(1). 235–251. 19 indexed citations

Agrawal, Sudhanshu, Wonjun Lee, Samy Wu Fung, & Levon Nurbekyan. (2022). Random features for high-dimensional nonlocal mean-field games. Journal of Computational Physics. 459. 111136–111136. 6 indexed citations

Chow, Yat Tin, Samy Wu Fung, Siting Liu, Levon Nurbekyan, & Stanley Osher. (2022). A numerical algorithm for inverse problem from partial boundary measurement arising from mean field game problem. Inverse Problems. 39(1). 14001–14001. 20 indexed citations

Liu, Siting, et al.. (2021). Computational Methods for First-Order Nonlocal Mean Field Games with Applications. SIAM Journal on Numerical Analysis. 59(5). 2639–2668. 26 indexed citations

10.

Jacobsen, Jörn-Henrik, et al.. (2020). How to Train Your Neural ODE: the World of Jacobian and Kinetic Regularization. International Conference on Machine Learning. 1. 3154–3164. 7 indexed citations

11.

Lin, A. T., Samy Wu Fung, Wuchen Li, Levon Nurbekyan, & Stanley Osher. (2020). Alternating the Population and Control Neural Networks to Solve High-Dimensional Stochastic Mean-Field Games. arXiv (Cornell University). 53 indexed citations

12.

Ruthotto, Lars, Stanley Osher, Wuchen Li, Levon Nurbekyan, & Samy Wu Fung. (2019). A Machine Learning Framework for Solving High-Dimensional Mean Field Game and Mean Field Control Problems. arXiv (Cornell University). 116 indexed citations

13.

Nurbekyan, Levon & João Saúde. (2019). Fourier approximation methods for first-order nonlocal mean-field games. Portugaliae Mathematica. 75(3). 367–396. 19 indexed citations

14.

Gomes, Diogo A., et al.. (2017). One-Dimensional Stationary Mean-Field Games with Local Coupling. Dynamic Games and Applications. 8(2). 315–351. 22 indexed citations

15.

Gomes, Diogo A., et al.. (2016). A mean-field game economic growth model. arXiv (Cornell University). 4693–4698. 3 indexed citations

16.

Nurbekyan, Levon, et al.. (2016). Regularity of solutions in semilinear elliptic theory. Bulletin of Mathematical Sciences. 7(1). 177–200. 7 indexed citations

17.

Gomes, Diogo A., et al.. (2016). Explicit solutions of one-dimensional, first-order, stationary mean-field games with congestion. King Abdullah University of Science and Technology Repository (King Abdullah University of Science and Technology). 4534–4539. 13 indexed citations

18.

Gomes, Diogo A. & Levon Nurbekyan. (2014). On the minimizers of calculus of variations problems in Hilbert spaces. Calculus of Variations and Partial Differential Equations. 52(1-2). 65–93. 5 indexed citations

19.

Nurbekyan, Levon & Diogo A. Gomes. (2012). Lagrangian dynamics and a weak KAM theorem on the d-infinite dimensional torus. 112(2). 152–159. 1 indexed citations

20.

Nurbekyan, Levon. (2012). Calculus of variations in Hilbert spaces. Journal of Contemporary Mathematical Analysis (Armenian Academy of Sciences). 47(3). 148–160.

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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