Sergey Dolgov
- Computational Mathematics top 0.1%
- Statistical and Nonlinear Physics top 2%
- Computational Theory and Mathematics top 2%
- Atomic and Molecular Physics, and Optics top 10%
- Computational Mechanics top 5%
- Co-authors
- Dmitry SavostyanovBoris N. KhoromskijIvan OseledetsMartin StollRaffaele BorrelliMichael LubaschDieter JakschTiangang Cui
- Topics
- Tensor decomposition and applications (37 papers)Model Reduction and Neural Networks (19 papers)Matrix Theory and Algorithms (12 papers)
- Cited by
- Computational MathematicsStatistical and Nonlinear PhysicsComputational Theory and Mathematics
- Partner nations
- United KingdomGermanyUnited States
In The Last Decade
Sergey Dolgov
45 papers receiving 925 citations
Peers
Comparison fields: 5 of 56
- Computational Mathematics 636
- Statistical and Nonlinear Physics 331
- Computational Theory and Mathematics 289
- Atomic and Molecular Physics, and Optics 243
- Computational Mechanics 218
Countries citing papers authored by Sergey Dolgov
This map shows the geographic impact of Sergey Dolgov'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 Sergey Dolgov with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sergey Dolgov more than expected).
Fields of papers citing papers by Sergey Dolgov
This network shows the impact of papers produced by Sergey Dolgov. 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 Sergey Dolgov. The network helps show where Sergey Dolgov may publish in the future.
Co-authorship network of co-authors of Sergey Dolgov
This figure shows the co-authorship network connecting the top 25 collaborators of Sergey Dolgov. A scholar is included among the top collaborators of Sergey Dolgov 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 Sergey Dolgov. Sergey Dolgov is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 3 | |
| 2 | 0 | |
| 3 | 2 | |
| 4 | 0 | |
| 5 | 17 | |
| 6 | 15 | |
| 7 | 4 | |
| 8 | 59 | |
| 9 | 11 | |
| 10 | 1 | |
| 11 | 2 | |
| 12 | 26 | |
| 13 | 11 | |
| 14 | 2 | |
| 15 | 27 | |
| 16 | 33 | |
| 17 | Tensor product methods in numerical simulation of high-dimensional dynamical problems | 9 |
| 18 | Fast solution of multi-dimensional parabolic problems in the tensor train/quantized tensor train–format with initial application to the Fokker-Planck equation | 12 |
| 19 | 7 | |
| 20 | Tensor Structured Iterative Solution of Elliptic Problems with Jumping Coefficients | 5 |
About Sergey Dolgov
Sergey Dolgov is a scholar working on Computational Mathematics, Statistical and Nonlinear Physics and Statistics, Probability and Uncertainty, having authored 48 papers that have together received 993 indexed citations. Recurring topics across this work include Tensor decomposition and applications (37 papers), Model Reduction and Neural Networks (19 papers) and Matrix Theory and Algorithms (12 papers). The work is most often cited by research in Computational Mathematics (636 citations), Statistical and Nonlinear Physics (331 citations) and Computational Theory and Mathematics (289 citations). Sergey Dolgov has collaborated with scholars based in United Kingdom, Germany and United States. Frequent co-authors include Dmitry Savostyanov, Boris N. Khoromskij, Ivan Oseledets, Martin Stoll, Raffaele Borrelli, Michael Lubasch, Dieter Jaksch, Tiangang Cui, Peter Benner and John W. Pearson. Their work appears in journals such as The Journal of Physical Chemistry B, Physical Review B and Journal of Computational Physics.
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.