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  1. Machine Learning Based Characterisation of Dissipative Solitons

    Кохановский, А. Ю., Беднякова, А. Е., Куприков, Е. А., Иваненко, А. В. & Турицын, С. К., 17 Oct 2019, 2019 Conference on Lasers and Electro-Optics Europe and European Quantum Electronics Conference, CLEO/Europe-EQEC 2019. Institute of Electrical and Electronics Engineers Inc., 1 p. 8873378. (2019 Conference on Lasers and Electro-Optics Europe and European Quantum Electronics Conference, CLEO/Europe-EQEC 2019).

    Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review

  2. Machine Learning Methods for Compensating Signal Distortions in Fiber-Optic Communication Lines

    Sidelnikov, O. S., Redyuk, A. A. & Fedoruk, M. P., Feb 2024, In: Optoelectronics, Instrumentation and Data Processing. 60, 1, p. 1-10 10 p.

    Research output: Contribution to journal › Article › peer-review

  3. Mapping of the pulse states of a fiber laser with ionic liquid gated carbon nanotube saturable absorber

    Kokhanovskiy, A., Petenev, I., Serebrenikov, K., Ivanenko, A., Kobtsev, S., Turitysin, S., Mkrtchyan, A. A., Gladush, Y. & Nasibulin, A. G., 2 Nov 2020, Proceedings - International Conference Laser Optics 2020, ICLO 2020. Institute of Electrical and Electronics Engineers Inc., 9285616. (Proceedings - International Conference Laser Optics 2020, ICLO 2020).

    Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review

  4. Master integrals for e+e− → 2γ process at large energies and angles

    Ли, Р. Н. & Стоцкий, В. А., Dec 2024, In: Journal of High Energy Physics. 2024, 12, 14 p., 106.

    Research output: Contribution to journal › Article › peer-review

  5. Mathematical and numerical models of two asymmetric gene networks

    Golubyatnikov, V. P., Kazantsev, M. V. E., Kirillova, N. E., Bukharina, T. A. E. & Furman, D. P., 1 Jan 2018, In: Сибирские электронные математические известия. 15, p. 1271-1283 13 p.

    Research output: Contribution to journal › Article › peer-review

  6. Mathematical Modeling and Forecasting of COVID-19 in Moscow and Novosibirsk Region

    Krivorot’ko, O. I., Kabanikhin, S. I., Zyat’kov, N. Y., Prikhod’ko, A. Y., Prokhoshin, N. M. & Shishlenin, M. A., Oct 2020, In: Numerical Analysis and Applications. 13, 4, p. 332-348 17 p.

    Research output: Contribution to journal › Article › peer-review

  7. Mathematical modeling of normal-pressure hydrocephalus at different levels of the brain geometry detalization

    Yan’kova, G. S., Cherevko, A. A., Khe, A. K., Bogomyakova, O. B. & Tulupov, A. A., Jul 2021, In: Journal of Applied Mechanics and Technical Physics. 62, 4, p. 654-662 9 p., 15.

    Research output: Contribution to journal › Article › peer-review

  8. Mathematical Modeling of the Wuhan COVID-2019 Epidemic and Inverse Problems

    Kabanikhin, S. I. & Krivorotko, O. I., Nov 2020, In: Computational Mathematics and Mathematical Physics. 60, 11, p. 1889-1899 11 p.

    Research output: Contribution to journal › Article › peer-review

  9. Mathematical Simulation of the Distribution of the Electron Beam Current during Pulsed Heating of a Metal Target

    Lazareva, G. G., Popov, V. A., Arakcheev, A. S., Burdakov, A. V., Schwab, I. V., Vaskevich, V. L., Maksimova, A. G., Ivashin, N. E. & Oksogoeva, I. P., Apr 2021, In: Journal of Applied and Industrial Mathematics. 15, 2, p. 292-301 10 p.

    Research output: Contribution to journal › Article › peer-review

  10. Mean field game for modeling of COVID-19 spread

    Petrakova, V. & Krivorotko, O., 1 Oct 2022, In: Journal of Mathematical Analysis and Applications. 514, 1, 126271.

    Research output: Contribution to journal › Article › peer-review

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