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Научни приноси на Николай Шегунов

Author ID (SCOPUS):57200576721

Researcher ID (Web of Science):AAL-4917-2021

ORCID ID:https://orcid.org/0000-0002-4691-935X
Статия в научно списание
Nikolay Shegunov, Oleg Iliev, On Dynamic Parallelization of Multilevel Monte Carlo Algorithm, Cybernetics and Information Technologies, vol:20, issue:6, 2020, pages:116-125, ISSN (print):1311-9702 , ISSN (online):1314-4081, doi:10.2478/cait-2020-0066, Ref, IR , SCOPUS, SJR (2.1 - 2019), SCOPUS Quartile: Q2 (2020), International 2020
Статия в поредица
I. Hristov, R. Hristova, S. Dimova, P. Armyanov, N. Shegunov, I. Puzynin, T. Puzynina, Z. Sharipov, Z. Tukhliev, On the efficient parallel computing of long term reliable trajectories for the Lorenz system, arXiv.org , 2021, doi:arXiv preprint arXiv:2101.06682, Ref, International 2021
Статия в сборник (на конференция и др.)
1 O. Iliev, N. Shegunov, P. Armyanov, A. Semerdzhiev, I. Christov, On to Parallel MLMC for Stationary Single Phase Flow Problem, 13th International Conference "Large Scale Scientific Computing", 2021, Ref, International, PhD 2021
2 Nikolay Shegunov, Petar Armyanov, Ivan Ivanov, Multi-threaded Approach for Generation of Random Boolean Networks, Proceedings of the thirteenth International Conference on Information Systems and Grid Technologies (ISGT’2020), Sofia, Bulgaria, May 29 – 30, 2020, editor/s:Vladimir Dimitrov, Vasil Georgiev, 2020, pages:193-199, ISSN (online):1613-0073, Ref, International, PhD 2020
3 Petr Zakharov, Oleg Iliev, Jan Mohring, Nikolay Shegunov, Parallel Multilevel Monte Carlo Algorithms for Elliptic PDEs with Random Coefficients, International Conference on Large-Scale Scientific Computing, Publisher:Springer, Cham, 2020, pages:463-472, Ref, International 2020
4 I. Hristov, R. Hristova, S. Dimova, P. Armyanov, N. Shegunov, I. Puzynin, T. Puzynina, Z. Sharipov, Z. Tukhliev, Parallelizing multiple precision Taylor series method for integrating the Lorenz system, arXiv.org , 2020, ISSN (online):2331-8422, Ref, International 2020
5 Peter Bastian, et al, Nikolay Shegunov, Software for Exascale Computing - SPPEXA 2016-2019, Exa-Dune—Flexible PDE Solvers, Numerical Methods and Applications, editor/s:Barth, T.J., Griebel, M., Keyes, D.E., Nieminen, R.M., Roose, D., Schlick, T., Publisher:Springer, 2020, pages:225-269, ISSN (online):1439-7358, ISBN:978-3-030-47956-5, doi:10.1007/978-3-030-47956-5, Ref, International 2020
6 S. Dimova, I. Hristov, R. Hristova, I. Puzynin, T. Puzynina, Z. Sharipov, N. Shegunov, Z.Tukhliev., OpenMP parallelization of multiple precision Taylor series method., Arxiv.org, 2019, Ref, International 2019
7 SN Dimova, IG Hristov, RD Hristova, IV Puzynin, TP Puzynina, ZA Sharipov, NG Shegunov, ZK Tukhliev, Combined Explicit-Implicit Taylor Series Methods, Proceedings of the VIII International Conference” Distributed Computing and Grid-technologies in Science and Education”(GRID 2018), 2018, pages:544-548, ISSN (online):1613-0073, Ref 2018
8 Nikolay Shegunov, Petar Armyanov, Atanas Semerdjiev, Oleg Iliev, GPU Accelerated Monte Carlo Sampling for SPDEs, Proceedings of the Information Systems and Grid Technologies, editor/s: Vladimir Dimitrov, Vasil Georgiev, 2018, pages:99-106, ISSN (online):1613-0073, Ref, SCOPUS, SJR (0.177 - 2019), International 2018
9 Oleg Iliev, Jan Mohring, Nikolay Shegunov, Renormalization Based MLMC Method for Scalar Elliptic SPDE, nternational Conference on Large-Scale Scientific Computing, Publisher:Springer, Cham, 2018, pages:295-303, Ref, International 2018
10 A. Avdzhieva, D. Aleksov, I. Hristov, N. Shegunov, P. Marinov, Circular arc spline approximation of pointwise curves for use in NC programming, 104-th European Study Group with Industry ESGI'104 September 23 - 27, 2014, Sofia, Bulgaria, Problems and Final Reports, Publisher:Demetra, 2014, pages:94-101, ISBN: 978-954-9526-87-5, PhD, MSc 2014
Участие в конференция
1 Присъствие, Nikolay Shegunov, Comparison Between Different Permeability Field Approximations in MLMC Applications 2019
2 Секционен доклад, Nikolay Shegunov, Toward exascale computations of uncertainty quantification for porous media flow using multilevel Monte Carlo 2016