Levon Nurbekyan
- Statistical and Nonlinear Physics top 5%
- Finance top 5%
- Artificial Intelligence
- Applied Mathematics top 10%
- Modeling and Simulation top 5%
- Co-authors
- Stanley OsherSamy Wu FungWuchen LiLars RuthottoDiogo A. GomesSiting LiuA. T. LinJoão Saúde
- Topics
- Stochastic processes and financial applications (12 papers)Model Reduction and Neural Networks (4 papers)Nonlinear Partial Differential Equations (4 papers)
- Partner nations
- United StatesSaudi ArabiaPortugal
In The Last Decade
Levon Nurbekyan
21 papers receiving 348 citations
Peers
Comparison fields: 5 of 54
- Statistical and Nonlinear Physics 129
- Finance 106
- Artificial Intelligence 79
- Applied Mathematics 51
- Modeling and Simulation 49
Countries citing papers authored by Levon Nurbekyan
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
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
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 3 | |
| 3 | 5 | |
| 4 | 11 | |
| 5 | 19 | |
| 6 | 6 | |
| 7 | 26 | |
| 8 | How to Train Your Neural ODE: the World of Jacobian and Kinetic Regularization | 7 |
| 9 | 53 | |
| 10 | 116 | |
| 11 | 19 | |
| 12 | 5 | |
| 13 | 22 | |
| 14 | 4 | |
| 15 | 7 | |
| 16 | 3 | |
| 17 | 13 | |
| 18 | 5 | |
| 19 | Lagrangian dynamics and a weak KAM theorem on the d-infinite dimensional torus | 1 |
| 20 | 0 |
About Levon Nurbekyan
Levon Nurbekyan is a scholar working on Finance, Modeling and Simulation and Applied Mathematics, having authored 23 papers that have together received 365 indexed citations. Recurring topics across this work include Stochastic processes and financial applications (12 papers), Model Reduction and Neural Networks (4 papers) and Nonlinear Partial Differential Equations (4 papers). The work is most often cited by research in Modeling and Simulation (49 citations), Finance (106 citations) and Statistical and Nonlinear Physics (129 citations). Levon Nurbekyan has collaborated with scholars based in United States, Saudi Arabia and Portugal. Frequent 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. Their work appears in journals such as Scientific Reports, Journal of Computational Physics and SIAM Journal on Numerical Analysis.
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.