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  1. Deep reinforcement learning algorithm for self-tuning 8-figure fiber laser

    Kokhanovskiy, A., Kuprikov, E., Serebrennikov, K. & Turitsyn, S., 2021, The European Conference on Lasers and Electro-Optics, CLEO/Europe 2021. The Optical Society, cj_4_3. (Optics InfoBase Conference Papers).

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

  2. Deep Reinforcement Learning Control of Cylinder Flow Using Rotary Oscillations at Low Reynolds Number

    Tokarev, M., Palkin, E. & Mullyadzhanov, R., 2 Nov 2020, In: Energies. 13, 22, 11 p., 5920.

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

  3. Deep Reinforcement Learning Control of Mode-Locked Fiber Laser

    Serebrennikov, K., Kuprikov, E. & Kokhanovskiy, A., 8 Dec 2025, In: Journal of Lightwave Technology. p. 1-7 7 p.

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

  4. Deep reinforcement learning for self-tuning laser source of dissipative solitons

    Kuprikov, E., Kokhanovskiy, A., Serebrennikov, K. & Turitsyn, S., Dec 2022, In: Scientific Reports. 12, 1, 7185.

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

  5. Deep Sources of Recent Volcanism in Armenia Inferred From Ambient Noise Tomography

    Meliksetian, K., Sargsyan, L., Koulakov, I., Toghramadjian, N., Belovezhets, N., Berezhnev, Y., Navasardyan, G., Grigoryan, E., Vasilevsky, A. & Sahakyan, E., Jan 2026, In: Journal of Geophysical Research: Solid Earth. 131, 1, 22 p., e2025JB032349.

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

  6. Deep sub-Doppler cooling of Mg in MOT formed by light waves with elliptical polarization

    Prudnikov, O. N., Brazhnikov, D. V., Taichenachev, A. V., Yudin, V. I. & Goncharov, A. N., 16 Feb 2017, In: Journal of Physics: Conference Series. 793, 1, 9 p., 012021.

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

  7. DEER/PELDOR Study of the Effect of Extremely Low Concentrations of the Antimicrobial Peptide Chalciporin A on the Membrane Lipid Organization

    Kashnik, A. S., Syryamina, V. N., Biondi, B., Peggion, C., Formaggio, F. & Dzuba, S. A., Mar 2023, In: Applied Magnetic Resonance. 54, 3, p. 401-414 14 p.

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

  8. Default mode network activity is related to efficiency in a combined motion error and gambling task

    Chernov, G. V., Mel’nikov, M. Y., Belianin, A. V., Rudych, P. D., Bezmaternykh, D. D. & Shakhzadayev, R. M., 6 Jan 2026, In: Scientific Reports. 16, 1, 13 p., 4422.

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

  9. Default Mode Network Connections Supporting Intra- Individual Variability in Typically Developing Primary School Children: An EEG Study

    Privodnova, E. Y., Slobodskaya, H. R., Bocharov, A. V., Saprigyn, A. E. & Knyazev, G. G., Oct 2020, In: Neuropsychology. 34, 7, p. 811-823 13 p.

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

  10. Defect induced photoluminescence and triboluminescence in layered CaLaAl3O7

    Li, Y., Li, Y., Koskin, I. P., Ma, Z., Benassi, E. & Wang, Z., 7 Apr 2020, In: Dalton Transactions. 49, 13, p. 3942-3945 4 p.

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

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