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  1. Machine learning based background rejection for Baikal-GVD neutrino telescope

    Kalashev, O., Kharuk, I. & Rubtsov, G., 2023, In: Journal of Physics: Conference Series. 2438, 1, 5 p., 012099.

    Research output: Contribution to journal › Conference article › peer-review

  2. Machine learning based characterisation of dissipative solitons

    Kokhanovskiy, A., Bednyakova, A., Kuprikov, E., Ivanenko, A. & Turitsyn, S., 1 Jan 2019, The European Conference on Lasers and Electro-Optics, CLEO_Europe_2019. OSA Publishing, 2019-cj_2_6. (Optics InfoBase Conference Papers; vol. Part F140-CLEO_Europe 2019).

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

  3. 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

  4. Machine Learning-Based Preconditioner to Solve Poisson Equation

    Chekmeneva, E., Khachova, T. & Lisitsa, V., 2026, Lecture Notes in Computer Science. Springer, p. 376-387 12 p. 25. (Lecture Notes in Computer Science; vol. 15888 LNCS).

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

  5. Machine learning-based predicting of the equilibrium cation distribution in faujasite-type zeolites

    Гренев, И. В., Бобков, М. Е., Иванов, А. Д., Uliankina, A. I. & Шубин, А. А., 1 Feb 2025, In: Materials Chemistry and Physics. 349, Part 2, 11 p., 131853.

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

  6. Machine learning-based pulse characterization in figure-eight mode-locked lasers

    Kokhanovskiy, A., Bednyakova, A., Kuprikov, E., Ivanenko, A., Dyatlov, M., Lotkov, D., Kobtsev, S. & Turitsyn, S., 1 Jul 2019, In: Optics Letters. 44, 13, p. 3410-3413 4 p.

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

  7. Machine Learning for Identifying Characteristics of Isolated, Clustered, and Pulsed Vapor Bubbles on a Heated Surface under Non-Stationary Boiling Conditions

    Khan, P. V., Levin, A. A., Chupin, I. I. & Safarov, A. S., 29 Dec 2025, In: Energy Systems Research. 8, 4, p. 54-64 11 p., 6.

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

  8. Machine learning for studying mineral-component composition of the Bazhenov Formation based on well-logging and core data

    Temnikova, E. Y., Grubas, S. I. & Glinskikh, V. N., Dec 2021, In: Neftyanoe khozyaystvo - Oil Industry. 12, p. 88-91 4 p., 17.

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

  9. 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

  10. Machine Learning Methods for Control of Fibre Lasers with Double Gain Nonlinear Loop Mirror

    Kokhanovskiy, A., Ivanenko, A., Kobtsev, S., Smirnov, S. & Turitsyn, S., 27 Feb 2019, In: Scientific Reports. 9, 1, p. 2916 7 p., 2916.

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

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