Sergei Kucherenko

4.3k total citations
67 papers, 2.5k citations indexed

About

Sergei Kucherenko is a scholar working on Statistics, Probability and Uncertainty, Mechanics of Materials and Numerical Analysis. According to data from OpenAlex, Sergei Kucherenko has authored 67 papers receiving a total of 2.5k indexed citations (citations by other indexed papers that have themselves been cited), including 36 papers in Statistics, Probability and Uncertainty, 20 papers in Mechanics of Materials and 16 papers in Numerical Analysis. Recurrent topics in Sergei Kucherenko's work include Probabilistic and Robust Engineering Design (35 papers), Mathematical Approximation and Integration (16 papers) and Fatigue and fracture mechanics (15 papers). Sergei Kucherenko is often cited by papers focused on Probabilistic and Robust Engineering Design (35 papers), Mathematical Approximation and Integration (16 papers) and Fatigue and fracture mechanics (15 papers). Sergei Kucherenko collaborates with scholars based in United Kingdom, Russia and Italy. Sergei Kucherenko's co-authors include I. M. Sobol, Stefano Tarantola, Nilay Shah, Paola Annoni, Wolfgang Mauntz, Debora Gatelli, C.C. Pantelides, María Rodriguez-Fernández, Shufang Song and Balázs Feil and has published in prestigious journals such as Acta Materialia, Industrial & Engineering Chemistry Research and Chemical Engineering Science.

In The Last Decade

Sergei Kucherenko

61 papers receiving 2.4k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Sergei Kucherenko United Kingdom 25 1.3k 572 410 392 279 67 2.5k
Toshimitsu Homma Japan 16 1.4k 1.0× 541 0.9× 335 0.8× 286 0.7× 324 1.2× 46 2.5k
Bertrand Iooss France 25 1.2k 0.9× 451 0.8× 538 1.3× 298 0.8× 303 1.1× 81 2.5k
F.J. Davis United States 6 1.1k 0.8× 521 0.9× 283 0.7× 263 0.7× 381 1.4× 17 2.5k
Stefano Marelli Switzerland 23 1.4k 1.1× 913 1.6× 479 1.2× 187 0.5× 265 0.9× 80 2.7k
Ivano Azzini Italy 8 879 0.7× 439 0.8× 210 0.5× 214 0.5× 423 1.5× 17 2.4k
David Gorsich United States 23 881 0.7× 724 1.3× 581 1.4× 175 0.4× 167 0.6× 173 2.2k
Laura Swiler United States 26 1.8k 1.4× 615 1.1× 1.1k 2.6× 303 0.8× 252 0.9× 103 4.5k
Edoardo Patelli United Kingdom 33 1.5k 1.1× 1.2k 2.1× 246 0.6× 393 1.0× 159 0.6× 181 3.2k
Houman Owhadi United States 22 935 0.7× 329 0.6× 651 1.6× 425 1.1× 177 0.6× 102 2.5k
T.G. Trucano United States 19 619 0.5× 309 0.5× 272 0.7× 270 0.7× 272 1.0× 64 2.5k

Countries citing papers authored by Sergei Kucherenko

Since Specialization
Citations

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

Fields of papers citing papers by Sergei Kucherenko

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sergei Kucherenko

This figure shows the co-authorship network connecting the top 25 collaborators of Sergei Kucherenko. A scholar is included among the top collaborators of Sergei Kucherenko 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 Sergei Kucherenko. Sergei Kucherenko 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.
Kucherenko, Sergei, Nilay Shah, & Oleksiy V. Klymenko. (2025). Analytical identification of process design spaces using R-functions. Computers & Chemical Engineering. 198. 109112–109112.
2.
3.
Lamboni, Matieyendou & Sergei Kucherenko. (2021). Multivariate sensitivity analysis and derivative-based global sensitivity measures with dependent variables. Reliability Engineering & System Safety. 212. 107519–107519. 28 indexed citations
4.
Zhou, Changcong, et al.. (2021). A unified approach for global sensitivity analysis based on active subspace and Kriging. Reliability Engineering & System Safety. 217. 108080–108080. 27 indexed citations
5.
Kucherenko, Sergei, et al.. (2019). Computationally efficient identification of probabilistic design spaces through application of metamodeling and adaptive sampling. Computers & Chemical Engineering. 132. 106608–106608. 24 indexed citations
6.
Kucherenko, Sergei & Shufang Song. (2017). Different numerical estimators for main effect global sensitivity indices. Reliability Engineering & System Safety. 165. 222–238. 49 indexed citations
7.
Kucherenko, Sergei. (2013). SOBOLHDMR: A General-Purpose Modeling Software. Methods in molecular biology. 1073. 191–224. 12 indexed citations
8.
Kucherenko, Sergei, Stefano Tarantola, & Paola Annoni. (2011). Estimation of global sensitivity indices for models with dependent variables. Computer Physics Communications. 183(4). 937–946. 239 indexed citations
9.
Sobol, I. M. & Sergei Kucherenko. (2010). A new derivative based importance criterion for groups of variables and its link with the global sensitivity indices. Computer Physics Communications. 181(7). 1212–1217. 82 indexed citations
10.
Hosseini, Seyed Ali, et al.. (2010). Multiscale Modeling of Hydrothermal Pretreatment: From Hemicellulose Hydrolysis to Biomass Size Optimization. Energy & Fuels. 24(9). 4673–4680. 18 indexed citations
11.
Sobol, I. M. & Sergei Kucherenko. (2009). Derivative based global sensitivity measures and their link with global sensitivity indices. Mathematics and Computers in Simulation. 79(10). 3009–3017. 297 indexed citations
12.
Kiparissides, Alexandros, Sergei Kucherenko, Athanasios Mantalaris, & Efstratios N. Pistikopoulos. (2009). Global Sensitivity Analysis Challenges in Biological Systems Modeling. Industrial & Engineering Chemistry Research. 48(15). 7168–7180. 105 indexed citations
13.
Kucherenko, Sergei, María Rodriguez-Fernández, C.C. Pantelides, & Nilay Shah. (2008). Monte Carlo evaluation of derivative-based global sensitivity measures. Reliability Engineering & System Safety. 94(7). 1135–1148. 204 indexed citations
14.
Kucherenko, Sergei, Pietro Belotti, Leo Liberti, & Nelson Maculan. (2007). New formulations for the Kissing Number Problem. Discrete Applied Mathematics. 155(14). 1837–1841. 15 indexed citations
15.
Sobol, I. M., Stefano Tarantola, Debora Gatelli, Sergei Kucherenko, & Wolfgang Mauntz. (2006). Estimating the approximation error when fixing unessential factors in global sensitivity analysis. Reliability Engineering & System Safety. 92(7). 957–960. 259 indexed citations
16.
Kucherenko, Sergei, et al.. (2005). Application of Deterministic Low-Discrepancy Sequences in Global Optimization. Computational Optimization and Applications. 30(3). 297–318. 65 indexed citations
17.
Kucherenko, Sergei, et al.. (2004). A flexible and generic approach to dynamic modelling of supply chains. Journal of the Operational Research Society. 55(8). 801–813. 22 indexed citations
18.
Kucherenko, Sergei, et al.. (2002). Regularities in temperature, magnetic field and pressure effect on the resistive properties of magnetic semiconductors. Journal of Magnetism and Magnetic Materials. 248(3). 396–401. 2 indexed citations
19.
Tang, Ting-wei, et al.. (1998). Time‐Dependent Solution of a Full Hydrodynamic ModelIncluding Convective Terms and Viscous Effect. VLSI design. 6(1-4). 173–176. 1 indexed citations
20.
Kudryashov, Nikolay A., et al.. (1990). Dynamics of light-induced diffraction gratings in silicon excited by picosecond pulses. Russian Physics Journal. 33(3). 246–250.

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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