Sergey Dolgov

1.8k total citations
48 papers, 993 citations indexed

About

Sergey Dolgov is a scholar working on Computational Mathematics, Statistical and Nonlinear Physics and Computational Theory and Mathematics. According to data from OpenAlex, Sergey Dolgov has authored 48 papers receiving a total of 993 indexed citations (citations by other indexed papers that have themselves been cited), including 37 papers in Computational Mathematics, 21 papers in Statistical and Nonlinear Physics and 12 papers in Computational Theory and Mathematics. Recurrent topics in Sergey Dolgov's work include Tensor decomposition and applications (37 papers), Model Reduction and Neural Networks (19 papers) and Matrix Theory and Algorithms (12 papers). Sergey Dolgov is often cited by papers focused on Tensor decomposition and applications (37 papers), Model Reduction and Neural Networks (19 papers) and Matrix Theory and Algorithms (12 papers). Sergey Dolgov collaborates with scholars based in United Kingdom, Germany and United States. Sergey Dolgov's 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 and has published in prestigious journals such as The Journal of Physical Chemistry B, Physical Review B and Journal of Computational Physics.

In The Last Decade

Sergey Dolgov

45 papers receiving 925 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Sergey Dolgov United Kingdom 17 636 331 289 243 218 48 993
Dmitry Savostyanov Russia 14 478 0.8× 202 0.6× 234 0.8× 127 0.5× 140 0.6× 24 648
Thorsten Rohwedder Germany 10 420 0.7× 178 0.5× 216 0.7× 190 0.8× 207 0.9× 13 690
Eugene E. Tyrtyshnikov Russia 11 349 0.5× 141 0.4× 240 0.8× 123 0.5× 187 0.9× 21 644
André Uschmajew Germany 16 522 0.8× 168 0.5× 262 0.9× 60 0.2× 311 1.4× 35 758
Martin J. Mohlenkamp United States 10 342 0.5× 141 0.4× 254 0.9× 185 0.8× 178 0.8× 19 783
E. E. Tyrtyshnikov Russia 18 596 0.9× 153 0.5× 706 2.4× 365 1.5× 355 1.6× 43 1.3k
Raf Vandebril Belgium 17 219 0.3× 170 0.5× 700 2.4× 225 0.9× 188 0.9× 120 1.1k
J. M. Landsberg United States 22 765 1.2× 203 0.6× 578 2.0× 52 0.2× 133 0.6× 80 1.6k
Thomas Huckle Germany 15 114 0.2× 70 0.2× 857 3.0× 628 2.6× 448 2.1× 62 1.2k
Awad H. Al-Mohy Saudi Arabia 8 22 0.0× 166 0.5× 407 1.4× 122 0.5× 148 0.7× 13 822

Countries citing papers authored by Sergey Dolgov

Since Specialization
Citations

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

20 of 20 papers shown
1.
Ochatt, Sergio, Melekşen Akın, Ming‐Tsair Chan, et al.. (2025). Research is rendering the recalcitrant woody plants amenable to biotechnological approaches. Plant Cell Tissue and Organ Culture (PCTOC). 161(2). 3 indexed citations
2.
Antil, Harbir, et al.. (2025). Smoothed Moreau‐Yosida Tensor‐Train Approximation of State‐Constrained Optimization Problems Under Uncertainty. Numerical Linear Algebra with Applications. 32(4).
3.
Dolgov, Sergey, et al.. (2024). Tensor quantum programming. New Journal of Physics. 26(12). 123019–123019. 2 indexed citations
4.
Dolgov, Sergey & Dmitry Savostyanov. (2024). Tensor product algorithms for inference of contact network from epidemiological data. BMC Bioinformatics. 25(1). 285–285.
5.
Dolgov, Sergey, et al.. (2023). Quantum state preparation using tensor networks. Quantum Science and Technology. 8(3). 35027–35027. 17 indexed citations
6.
Dolgov, Sergey, et al.. (2023). Data-Driven Tensor Train Gradient Cross Approximation for Hamilton–Jacobi–Bellman Equations. SIAM Journal on Scientific Computing. 45(5). A2153–A2184. 15 indexed citations
7.
Dolgov, Sergey & Dmitry Savostyanov. (2023). Tensor product approach to modelling epidemics on networks. Applied Mathematics and Computation. 460. 128290–128290. 4 indexed citations
8.
Lubasch, Michael, Sergey Dolgov, Hessam Babaee, et al.. (2022). A quantum-inspired approach to exploit turbulence structures. Nature Computational Science. 2(1). 30–37. 59 indexed citations
9.
Dolgov, Sergey, et al.. (2022). Rank Bounds for Approximating Gaussian Densities in the Tensor-Train Format. SIAM/ASA Journal on Uncertainty Quantification. 10(3). 1191–1224. 11 indexed citations
10.
Fox, Colin, et al.. (2021). Grid methods for Bayes-optimal continuous-discrete filtering and utilizing a functional tensor train representation. Inverse Problems in Science and Engineering. 29(8). 1199–1217. 1 indexed citations
11.
Dolgov, Sergey & Tomáš Vejchodský. (2020). Guaranteed a posteriori error bounds for low-rank tensor approximate solutions. IMA Journal of Numerical Analysis. 41(2). 1240–1266. 2 indexed citations
12.
Lubasch, Michael, et al.. (2020). Parallel time-dependent variational principle algorithm for matrix product states. Physical review. B.. 101(23). 26 indexed citations
13.
Benner, Peter, Sergey Dolgov, Venera Khoromskaia, & Boris N. Khoromskij. (2017). Fast iterative solution of the Bethe–Salpeter eigenvalue problem using low-rank and QTT tensor approximation. Journal of Computational Physics. 334. 221–239. 11 indexed citations
14.
Dolgov, Sergey, Vladimir Kazeev, & Boris N. Khoromskij. (2017). Direct tensor-product solution of one-dimensional elliptic equations with parameter-dependent coefficients. Mathematics and Computers in Simulation. 145. 136–155. 2 indexed citations
15.
Dolgov, Sergey, John W. Pearson, Dmitry Savostyanov, & Martin Stoll. (2015). Fast tensor product solvers for optimization problems with fractional differential equations as constraints. Applied Mathematics and Computation. 273. 604–623. 27 indexed citations
16.
Dolgov, Sergey, Boris N. Khoromskij, Alexander Litvinenko, & Hermann G. Matthies. (2015). Polynomial Chaos Expansion of Random Coefficients and the Solution of Stochastic Partial Differential Equations in the Tensor Train Format. SIAM/ASA Journal on Uncertainty Quantification. 3(1). 1109–1135. 33 indexed citations
17.
Dolgov, Sergey. (2014). Tensor product methods in numerical simulation of high-dimensional dynamical problems. Qucosa (Saxon State and University Library Dresden). 9 indexed citations
18.
Dolgov, Sergey, Boris N. Khoromskij, & Ivan Oseledets. (2012). Fast solution of multi-dimensional parabolic problems in the tensor train/quantized tensor train–format with initial application to the Fokker-Planck equation. SIAM Journal on Scientific Computing. 34(6). 12 indexed citations
19.
Dolgov, Sergey & Boris N. Khoromskij. (2012). Two-level Tucker-TT-QTT format for optimized tensor calculus. Max Planck Institute for Plasma Physics. 7 indexed citations
20.
Dolgov, Sergey, Boris N. Khoromskij, Ivan Oseledets, & Eugene E. Tyrtyshnikov. (2010). Tensor Structured Iterative Solution of Elliptic Problems with Jumping Coefficients. 5 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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