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  1. Estimating well killing fluid volume in the conditions of fractured porous reservoirs based on physical and mathematical modeling and statistical analysis

    Karmushin, S. R., Lezhnev, K. E., Gumerov, R. R., Bazyrov, I. S., Gunkin, A. S. & Gvritishvili, T. T., 2021, In: Neftyanoe khozyaystvo - Oil Industry. 12, p. 30-33 4 p., 6.

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

  2. Estimates of Implementation Complexity for Quantum Cryptanalysis of Post-Quantum Lattice-Based Cryptosystems

    Bakharev, A. O., Sept 2023, In: Journal of Applied and Industrial Mathematics. 17, 3, p. 459-482 24 p., 1.

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

  3. Erratum to: Structure and Shape of Hematite Particles Obtained by Oxidative Thermolysis of Iron Oxalate Dihydrate: Anisotropic Broadening of X-Ray Diffraction Peaks (Journal of Structural Chemistry, (2024), 65, 4, (655-665), 10.1134/S0022476624040024)

    Cherepanova, S. V., Sinitsa, N. A., Yatsenko, D. A., Gerasimov, E. Y., Sidel’nikov, A. A. & Matvienko, A. A., Aug 2024, In: Journal of Structural Chemistry. 65, 8, p. 1677 1 p.

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

  4. Erratum to: Multi-Class Surface Generation of Complex Anatomical Structures Using Neural Networks

    Epifanov, R. U. I., Федотова, Я. В., Popov, D. R. & Мулляджанов, Р. И., Dec 2025, In: Doklady Mathematics. 112, 3, p. 636 1 p.

    Research output: Contribution to journal › Comment/debate › peer-review

  5. ERANNs: Efficient residual audio neural networks for audio pattern recognition

    Verbitskiy, S., Berikov, V. & Vyshegorodtsev, V., Sept 2022, In: Pattern Recognition Letters. 161, p. 38-44 7 p.

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

  6. Environment-Agnostic IRM via Unsupervised Clustering and Adaptive Penalty Scaling

    Miron, B. & Bondarenko, I., 2026, Advances in Neural Computation, Machine Learning, and Cognitive Research IX. Kryzhanovsky, B., Dunin-Barkowski, W., Redko, V., Tiumentsev, Y. & Klimov, V. V. (eds.). Springer, p. 47-63 17 p. 5. (Studies in Computational Intelligence; vol. 1241 SCI).

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

  7. Enhancing the stability of physics-informed neural networks applied to convection problems

    Tsgoev, C. A., Bratenkov, M. A., Sakharov, D. I., Travnikov, V. A., Seredkin, A. V., Kalinin, V. A., Fomichev, D. V. & Mullyadzhanov, R. I., Mar 2025, In: Thermophysics and Aeromechanics. 32, 2, p. 449-463 15 p.

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

  8. Enhancing Stability of the Weakly Supervised Regression Algorithm Using Manifold Regularization and Fuzzy Clustering

    Kalmutskiy, K. & Berikov, V., 6 Apr 2025, In: Pattern Recognition and Image Analysis. 35, 1, p. 16-18 3 p.

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

  9. Enhancement of Rans Models by Means of the Tensor Basis Random Forest for Turbulent Flows in Two-Dimensional Channels with Bumps

    Bernard, A. & Yakovenko, S. N., Jun 2023, In: Journal of Applied Mechanics and Technical Physics. 64, 3, p. 437-441 5 p.

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

  10. Enhancement of Consistent Depth Estimation for Monocular Videos Approach

    Свейлам, М. Н. Х. & Толстокулаков, Н. Ю., 26 Jun 2021, Computer Science & Information Technology (CS & IT). p. 110-115 11 p.

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

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