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  1. 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

  2. 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

  3. 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

  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 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

  6. 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

  7. 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

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

    Kokhanovskiy, A., Kuprikov, E., Serebrennikov, K. & Turitsyn, S., Jun 2021, 2021 Conference on Lasers and Electro-Optics Europe and European Quantum Electronics Conference, CLEO/Europe-EQEC 2021. Institute of Electrical and Electronics Engineers Inc., 1 p. (2021 Conference on Lasers and Electro-Optics Europe and European Quantum Electronics Conference, CLEO/Europe-EQEC 2021).

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

  9. Deep-red phosphorescent organic–inorganic hybrid Mn(II) complexes based on 2-(diphenylphosphoryl)-N,N-diethylacetamide ligand

    Artem'ev, A. V., Berezin, A. S., Brel, V. K., Morgalyuk, V. P. & Samsonenko, D. G., 1 Jul 2018, In: Polyhedron. 148, p. 184-188 5 p.

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

  10. Deep Neural Networks with Time-Domain Synthetic Photonic Lattices

    Pankov, A. V., Sidelnikov, O. S., Vatnik, I. D., Churkin, D. V. & Sukhorukov, A. A., Jun 2021, 2021 Conference on Lasers and Electro-Optics Europe and European Quantum Electronics Conference, CLEO/Europe-EQEC 2021. Institute of Electrical and Electronics Engineers Inc., (2021 Conference on Lasers and Electro-Optics Europe and European Quantum Electronics Conference, CLEO/Europe-EQEC 2021).

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

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