Publications

2026

1. Salloum, H., Mifsud Bonici, M., Ibrahim, S., Osinenko, P., & Kornaev, A. (2026). Diversity-Aware Adaptive Collocation for Physics-Informed Neural Networks via Sparse QUBO Optimization and Hybrid Coresets. AI&PDE: ICLR 2026 Workshop on AI and Partial Differential Equations. https://openreview.net/forum?id=PcZbVU4Ap7

   Paper Preprint
2. Degtiarev, D., Ryabkova, A., & Kornaev, A. (2026). LLM-Driven Loss Balancing for Physics-Informed Neural Networks. AI&PDE: ICLR 2026 Workshop on AI and Partial Differential Equations. https://openreview.net/forum?id=XAUDCvtSSz

   Paper
3. Nurtdinov, D., Kornaev, A., & Maloletov, A. (2026). RL Bootstrapping of OpenVLA-OFT for a Novel Robot Embodiment. In arXiv preprint arXiv:2608.01013. arXiv. https://arxiv.org/abs/2608.01013

   Paper
4. Kornaeva, E. P., Kornaev, A. V., & Stebakov, I. N. (2026). Variational Physics-Informed Neural Network for Non-Newtonian Fluid Flow. Russian Journal of Nonlinear Dynamics, 22(3), 661–675. https://doi.org/10.20537/nd260405

   Paper
5. Rodichev, A. Y., Savin, L. A., Rodicheva, I. V., & Gryadunova, E. N. (2026). PREVENTIVE DIAGNOSTICS OF SLIDING BEARING UNITS. World of Transport and Technological Machines, 24(2(93)), 67–75. https://doi.org/10.33979/2073-7432-2026-2(93)-67-75

   Paper
6. Kazakov, Y., Savin, L., & Kornaev, A. (2026). Cartography-guided adaptive replay for robust reinforcement learning in nonlinear engineering control systems. Elsevier BV. https://doi.org/10.2139/ssrn.7656103

   Paper

2025

1. Kornaev, A., Lvov, D., Pershin, I., Kiselev, S., Afonchikov, D., Bariev, I., & Ibragimov, B. (2025). Automatic Calculation of Cardiometric Coefficients on Chest X-Ray Images. IEEE Access, 13, 10702–10712. https://doi.org/10.1109/access.2024.3524116

   Paper
2. Nurtdinov, D., Korshuk, A., Kornaev, A., & Maloletov, A. (2025). Evaluation of a Robust Control System in Real-World Cable-Driven Parallel Robots. ICLR 2025 Workshop. Towards Agentic AI for Science: Hypothesis Generation, Comprehension, Quantification, and Validation. https://openreview.net/forum?id=bK20ivFGKE

   Paper Preprint
3. Stebakov, I., Kornaev, A., & Kornaeva, E. (2025). Flow Domain Parameterization and Training of Generalized Physics-Informed Neural Networks for Solving Navier-Stokes Equations. ICLR 2025 Workshop on Machine Learning Multiscale Processes. https://openreview.net/forum?id=xqjs2ideRD

   Paper
4. Lvov, D., Stebakov, I., Kornaev, A., Pershin, I., Mustafaev, T., Afonchikov, D., Kuleev, R., Bariev, I., & Ibragimov, B. (2025). Uncertainty Estimation in Cardio Landmark Detection and Heart Disease Diagnosis on Chest X-Ray Images. IEEE Access, 13, 87229–87240. https://doi.org/10.1109/access.2025.3566772

   Paper

2024

1. Bryanskaya, E. O., Dremin, V. V., Shupletsov, V. V., Kornaev, A. V., Kirillin, M. Y., Bakotina, A. V., Panchenkov, D. N., Podmasteryev, K. V., Artyushenko, V. G., & Dunaev, A. V. (2024). Erratum to “Digital diaphanoscopy of maxillary sinus pathologies supported by machine learning.” Journal of Biophotonics, 17(1), e202300469. https://doi.org/10.1002/jbio.202300469

   Paper
2. Kornaev, A., Makovik, I., Kornaeva, E., Volkov, M., Margaryants, N., Potapova, E., Stebakov, I., Fisyun, I., Afonchikov, D., Pershin, I., Dremin, V., Ibragimov, B., Kuleev, R., Gevorkyan, T., & Dunaev, A. (2024). Approaching in vivo blood count tests with capillaroscopy and deep learning. Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.36227/techrxiv.172840607.74914650/v1

   Paper
3. Stebakov, I., Kornaev, A., Kornaeva, E., Litvinenko, N., Kazakov, Y., Ivanov, O., & Ibragimov, B. (2024). Artificial Neural Networks as a Natural Tool in Solution of Variational Problems in Hydrodynamics. IEEE Access, 12, 169945–169954. https://doi.org/10.1109/access.2024.3498437

   Paper
4. Kornaev, A., Kornaeva, E., Ivanov, O., Pershin, I., & Alukaev, D. (2024). Awareness of uncertainty in classification using a multivariate model and multi-views. In arXiv preprint arXiv:2404.10314. arXiv. https://arxiv.org/abs/2404.10314

   Paper
5. Stebakov, I., Kornaev, A., Kornaeva, E., & Majorov, S. (2024). Exploring Variational Methods in Hydro- and Hemodynamics Through Incompressible Velocity Distribution Sampling. IEEE Access, 12, 191109–191119. https://doi.org/10.1109/access.2024.3517143

   Paper
6. Afonchikov, D., Kornaeva, E., Makovik, I., & Kornaev, A. (2024). Native machine learning for noisy microscopic data processing. The Second Tiny Papers Track at ICLR 2024. https://openreview.net/forum?id=5sWY5ENwtl

   Paper Preprint
7. Serenko, I. A., Dorn, Y. V., Singh, S. R., & Kornaev, A. V. (2024). Room for Uncertainty in Remaining Useful Life Estimation for Turbofan Jet Engines. Russian Journal of Nonlinear Dynamics, 20(5), 933–943. https://doi.org/10.20537/nd241218

   Paper

2023

1. Bryanskaya, E. O., Dremin, V. V., Shupletsov, V. V., Kornaev, A. V., Kirillin, M. Y., Bakotina, A. V., Panchenkov, D. N., Podmasteryev, K. V., Artyushenko, V. G., & Dunaev, A. V. (2023). Digital diaphanoscopy of maxillary sinus pathologies supported by machine learning. Journal of Biophotonics, 16(9), e202300138. https://doi.org/10.1002/jbio.202300138

   Paper
2. Kornaeva, E. P., Stebakov, I. N., Kornaev, A. V., Dremin, V. V., Popov, S. G., & Vinokurov, A. Y. (2023). A method to measure non-Newtonian fluids viscosity using inertial viscometer with a computer vision system. International Journal of Mechanical Sciences, 242, 107967. https://doi.org/10.1016/j.ijmecsci.2022.107967

   Paper
3. Li, S., Zhou, C., Savin, L., Shutin, D., Kornaev, A., Polyakov, R., & Chen, Z. (2023). Theoretical and experimental study of motion suppression and friction reduction of rotor systems with active hybrid fluid-film bearings. Mechanical Systems and Signal Processing, 182, 109548. https://doi.org/10.1016/j.ymssp.2022.109548

   Paper
4. Alukaev, D., Kiselev, S., Pershin, I., Ibragimov, B., Ivanov, V., Kornaev, A., & Titov, I. (2023). Cross-Modal Conceptualization in Bottleneck Models. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 5241–5253. https://doi.org/10.18653/v1/2023.emnlp-main.318

   Paper
5. Mongolin, A., Khomeriki, S., Karnaukhov, N., Abramov, K., Vorobev, R., Gorbachev, Y., Zabruntseva, A., & Kornaev, A. (2023). Influence of Cross Histology Transfer Learning on the Accuracy of Medical Diagnostics Systems. In Innovations in Bio-Inspired Computing and Applications (pp. 926–931). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-27499-2_86

   Paper
6. Kornaeva, E., Kornaev, A., Fetisov, A., Stebakov, I., & Savin, L. (2023). U-Net as a Tool for Adjusting the Velocity Distributions of Rheomagnetic Fluids. In Intelligent Systems Design and Applications (pp. 8–16). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-35510-3_2

   Paper

2022

1. Stebakov, I., Kornaev, A., Popov, S., & Savin, L. (2022). Fault diagnosis systems for rotating machines operating with fluid-film bearings. Proceedings of the Institution of Mechanical Engineers, Part J: Journal of Engineering Tribology, 236(10), 1917–1931. https://doi.org/10.1177/13506501211073242

   Paper
2. Kazakov, Y. N., Kornaev, A. V., Shutin, D. V., Li, S., & Savin, L. A. (2022). Active fluid-film bearing with deep Q-network agent-based control system. Journal of Tribology, 144(8), 081803. https://doi.org/10.1115/1.4053776

   Paper
3. Kornaev, A. V., Dremin, V. V., Kornaeva, E. P., & Volkov, M. V. (2022). Application of deep convolutional and long short-term memory neural networks to red blood cells motion detection and velocity approximation. Computational Biophysics and Nanobiophotonics, 12194, 78–80.
4. Kornaev, A. V., Nikanov, I., & Kuleev, R. (2022). Intersectoral artificial intelligence technologies: search for and implementation of efficient solutions. Doklady Mathematics, 106(Suppl 1), S4–S8. https://doi.org/10.1134/s1064562422060114

   Paper
5. Kazakov, Y., Stebakov, I., Fetisov, A., Kornaev, A., & Polyakov, R. (2022). Methods for Improving the Fault Diagnosis Accuracy of Rotating Machines. International Conference on Intelligent Systems Design and Applications, 110–119.
6. Kornaeva, E., Kornaev, A., Fetisov, A., Stebakov, I., & Ibragimov, B. (2022). Physics-based loss and machine learning approach in application to non-Newtonian fluids flow modeling. 2022 IEEE Congress on Evolutionary Computation (CEC), 1–8. https://doi.org/10.1109/cec55065.2022.9870411

   Paper
7. Savin, L., Shutin, D., Kazakov, Y., Kornaev, A., Polyakov, R., & Li, S. (2022). Problems of Development of Tribotronics: Control and Machine Learning Techniques. Workshop on Active Bearings in Rotating Machinery, 12–26. https://doi.org/10.1007/978-3-031-32394-2_2

   Paper
8. Kazakov, Y. N., Kornaev, A. V., Shutin, D. V., Kornaeva, E. P., & Savin, L. A. (2022). Reducing rotor vibrations in active conical fluid film bearings with controllable gap. Russian Journal of Nonlinear Dynamics, 18(5), 873–883.
9. Kornaev, A. V., Kornaeva, E. P., & Stebakov, I. (2022). Theoretical prerequisites for physically justified machine learning and its applications to fluid dynamics. Doklady Mathematics, 106(Suppl 1), S91–S92. https://doi.org/10.1134/s1064562422060126

   Paper
10. Казаков, Ю. Н., Корнаев, А. В., Шутин, Д. В., & Савин, Л. А. (2022). АДАПТИВНЫЙ ПИ-КОНТРОЛЛЕР ДЛЯ КОНТРОЛЯ ПОЛОЖЕНИЕМ РОТОРА. Энерго-и Ресурсосбережение-XXI Век, 150–155.
11. Стебаков, И. Н., Корнаева, Е. П., & Корнаев, А. В. (2022). Интеллектуальная система диагностики роторной машины. IX Международная Научно-Практическая Конференция Имени Академика АГ Шипунова, 82–87.
12. Стебаков, И. Н., Корнаева, Е. П., Попов, С. Г., & Корнаев, А. В. (2022). МЕХАТРОННАЯ УСТАНОВКА ДЛЯ ИССЛЕДОВАНИЯ РЕОЛОГИЧЕСКИХ СВОЙСТВ ФИЗИОЛОГИЧЕСКИХ ЖИДКОСТЕЙ. Перспективы Отраслевого Взаимодействия в Комплексной Реабилитации, 119–125.
13. Корнаева, Е. П., Стебаков, И. Н., Корнаев, А. В., & Дрёмин, В. В. (2022). Моделирование течения неньютоновских жидкостей в тороидальном канале инерционного вискозиметра с системой технического зрения. Известия Юго-Западного Государственного Университета, 26(1), 129–147.

2021

1. Kornaeva, E., & Kornaev, A. (2021). Application of convolutional neural networks to rotary machines state recognition. IOP Conference Series: Materials Science and Engineering, 1047(1), 012144. https://doi.org/10.1088/1757-899x/1047/1/012144

   Paper
2. Kornaev, A. V., Kornaeva, E. P., Savin, L. A., Kazakov, Y. N., Fetisov, A., Rodichev, A. Y., & Mayorov, S. V. (2021). Enhanced hydrodynamic lubrication of lightly loaded fluid-film bearings due to the viscosity wedge effect. Tribology International, 160, 107027.
3. Fetisov, A., & Kornaev, A. (2021). Journal bearing with variable dynamic characteristics: simulation results and verification. Fund. Appl. Prob. Eng. Technol., 2(346), 140–145.
4. Fetisov, A., Kornaev, A., & Tyurin, V. (2021). Modeling the Magnetorheological Fluid Flow Between Parallel Plates Under an External Magnetic Field. International Conference on Industrial Engineering, 860–866.
5. Fetisov, A., Kazakov, Y. N., & Kornaev, A. (2021). Study of Trajectories of Rotors in Journal Bearings Lubricated with Aerated Liquids. International Conference on Industrial Engineering, 1271–1277.
6. Стебаков, И. Н., Корнаев, А. В., & Корнаева, Е. П. (2021). Интеллектуальная диагностика неисправностей роторных систем. Энерго-и Ресурсосбережение-XXI Век, 100–105.
7. Корнаева, Е., Корнаев, А., Стебаков, И., Попов, С., Ставцев, Д., & Дремин, В. (2021). Концепция мехатронной установки для исследования реологических свойств физиологических жидкостей. Фундаментальные и Прикладные Проблемы Техники и Технологии, 1, 83.

2020

1. Kornaev, A. V., Savin, L. A., & Kornaeva, E. P. (2020). Influence of polymer additives on friction in fluid-film bearings: Theoretical view on experimental results by Moritsugu Kasai et al. Proceedings of the Institution of Mechanical Engineers, Part J: Journal of Engineering Tribology, 234(6), 858–872. https://doi.org/10.1177/1350650119879260

   Paper
2. Babin, A., Kornaev, A., Rodichev, A., & Savin, L. (2020). Active thrust fluid-film bearings: Theoretical and experimental studies. Proceedings of the Institution of Mechanical Engineers, Part J: Journal of Engineering Tribology, 234(2), 261–273. https://doi.org/10.1177/1350650119862074

   Paper
3. Kornaeva, E., Kornaev, A., & Egorov, S. (2020). Application of artificial neural networks to solution of variational problems in hydrodynamics. Journal of Physics: Conference Series, 1553(1), 012005. https://doi.org/10.1088/1742-6596/1553/1/012005

   Paper
4. Kornaeva, E., Kornaev, A., Kazakov, Y. N., & Polyakov, R. (2020). Application of artificial neural networks to diagnostics of fluid-film bearing lubrication. IOP Conference Series: Materials Science and Engineering, 734(1), 012154. https://doi.org/10.1088/1757-899x/734/1/012154

   Paper
5. Polyakov, R., Kornaev, A., Bondarenko, M., & Shutin, D. (2020). Design features of hybrid bearings with adjustable stiffness. IOP Conference Series: Materials Science and Engineering, 734(1), 012080. https://doi.org/10.1088/1757-899x/734/1/012080

   Paper
6. Kornaev, A., Savin, L., Kornaev, N., Zaretsky, R., Kornaeva, E., Babin, A., & Stebakov, I. (2020). Machine learning for rotating machines: simulation, diagnosis and control. Vibroengineering Procedia, 32, 223–228.
7. Kornaev, A., & Kornaeva, E. (2020). Room for doubt as a way to improve the accuracy of machine learning algorithms. 2020 4th Scientific School on Dynamics of Complex Networks and Their Application in Intellectual Robotics (DCNAIR), 135–137.
8. Kazakov, Y., Kornaev, A., & Polyakov, R. (2020). Rotary machines diagnosis systems based on feed forward neural networks. 2020 International Russian Automation Conference (RusAutoCon), 336–340.
9. КАЗАКОВ, Ю. Н., & КОРНАЕВ, А. В. (2020). КОНЦЕПЦИЯ МЕХАТРОННОГО РОТОРНО-ОПОРНОГО КОМПЛЕКСА С УПРАВЛЯЕМЫМИ ХАРАКТЕРИСТИКАМИ РОТОРА НА ОСНОВЕ КОНИЧЕСКОГО ПОДШИПНИКА ЖИДКОСТНОГО ТРЕНИЯ. Юность и Знания-Гарантия Успеха-2020, 194–200.
10. Корнаев, А. В., & Бобырь, М. В. (2020). МОЛЕКУЛЯРНЫЙ ПОДШИПНИК ЖИДКОСТНОГО ТРЕНИЯ. Энерго-и Ресурсосбережение-XXI Век, 177–180.
11. Корнаева, Е. П., & Корнаев, А. В. (2020). Статистическая модель выявления аномальных данных в задачах диагностики. Вестник Ростовского Государственного Университета Путей Сообщения, (3 (79)), 177–184.

2019

1. Fetisov, A., Kornaev, A., & Tokmakov, N. (2019). Friction and Vibration in Journal Bearings Operating with Nanofluids: Experimental Results. Proceedings of the 4th International Conference on Industrial Engineering, 1087–1095. https://doi.org/10.1007/978-3-319-95630-5_114

   Paper
2. Kornaev, A. (2019). On Proof of the Generalized Lagrange Variational Principle. Proceedings of the 4th International Conference on Industrial Engineering, 1071–1078. https://doi.org/10.1007/978-3-319-95630-5_112

   Paper
3. Kornaev, A., Zaretsky, R., & Egorov, S. (2019). Simulation of Deep Learning Control Systems to Reduce Energy Loses due to Vibration and Friction in Rotor Bearings. 2019 3rd School on Dynamics of Complex Networks and Their Application in Intellectual Robotics (DCNAIR), 89–91. https://doi.org/10.1109/dcnair.2019.8875575

   Paper
4. Kornaev, A., Kornaeva, E., Savin, L., & Fetisov, A. (2019). Application of variational approach to non-Newtonian fluid flow modelling. Proccedings of International Scientific Conference “BALTTRIB 2019,” 1, 194–201. https://doi.org/10.15544/balttrib.2019.31

   Paper
5. Babin, A., Savin, L., & Kornaev, A. (2019). Evaluation of energy efficiency of thrust hybrid bearings with active control of lubrication regime. Proccedings of International Scientific Conference “BALTTRIB 2019,” 1, 166–173. https://doi.org/10.15544/balttrib.2019.27

   Paper
6. Savin, L., Kornaev, A., Polyakov, R., & Babin, A. (2019). Tribomechatronics as a field of study. Proccedings of International Scientific Conference “BALTTRIB 2019,” 1, 138–144. https://doi.org/10.15544/balttrib.2019.23

   Paper
7. Корнаева, Е., Корнаев, А., & Савин, Л. (2019). Моделирование неизотермического течения вязкой жидкости в конфузорных каналах в условиях многозонной подачи смазочного материала. Известия Юго-Западного Государственного Университета, 22(5), 109–118.
8. Фетисов, А. С., Корнаева, Е. П., Корнаев, А. В., & Казаков, Ю. Н. (2019). Экспериментальный стенд для исследования эффектов температурного клина. Известия Тульского Государственного Университета. Технические Науки, (12), 320–323.

2018

1. Babin, A., Savin, L., & Kornaev, A. (2018). Numerical and experimental study of active thrust fluid-film bearings with fixed PADS. 73rd Society of Tribologists and Lubrication Engineers Annual Meeting and Exhibition 2018, 257–259. http://www.scopus.com/inward/record.url?eid=2-s2.0-85052635365&partnerID=MN8TOARS

   Paper
2. Бабин, А., Корнаев, А., & Савин, Л. (2018). Статические и динамические характеристики упорных гидродинамических подшипников при смазке средами сложной реологии. Известия Юго-Западного Государственного Университета. Серия: Техника и Технологии, 8(1), 81–89.
3. Корнаев, А. В., & Корнаева, Е. П. (2018). Теоретическое обоснование повышения энергоэффективности опор жидкостного трения за счет создания температурного клина в смазочном слое. Энерго-и Ресурсосбережение-XXI Век, 161–165.

2017

1. Kornaev, A. V., Kornaev, N. V., Kornaeva, E. P., & Savin, L. A. (2017). Application of Artificial Neural Networks to Calculation of Oil Film Reaction Forces and Dynamics of Rotors on Journal Bearings. International Journal of Rotating Machinery, 2017(1), 1–11. https://doi.org/10.1155/2017/9196701

   Paper
2. Savin, L. A., Kornaev, A. V., & Kornaeva, E. P. (2017). Influence of Critical Flow Rates on Characteristics of Enforced and Shear Flows in Circular Convergent-Divergent Channels. International Journal of Rotating Machinery, 2017(1), 1–8. https://doi.org/10.1155/2017/8761375

   Paper
3. Kornaev, A. (2017). Theoretical premises of thermal wedge effect in fluid-film bearings supplied with a non-homogeneous lubricant. International Journal of Mechanics. http://www.scopus.com/inward/record.url?eid=2-s2.0-85042712346&partnerID=MN8TOARS

   Paper
4. Корнаев, А., Корнаева, Е., & Савин, Л. (2017). Повышение точности численного решения уравнения Рейнольдса посредством естественной адаптации сетки в бицилиндрических координатах. Известия Юго-Западного Государственного Университета. Серия: Управление, Вычислительная Техника, Информатика. Медицинское Приборостроение, 7(4), 49–58.
5. Корнаев, А. В., Корнаев, Н. В., Корнаева, Е. П., & Савин, Л. А. (2017). Построение траекторий движения ротора с применением нейросетевого программного модуля. Транспортное Машиностроение, (3 (56)), 20–28.

2016

1. Kornaev, A., Savin, L., Kornaeva, E., & Fetisov, A. (2016). Influence of the ultrafine oil additives on friction and vibration in journal bearings. Tribology International, 101, 131–140. https://doi.org/10.1016/j.triboint.2016.04.014

   Paper
2. Savin, L., Kornaev, A., & Kornaeva, E. (2016). Approximate solution of the Navier-Stokes equation and magnus effect in the fluid-film bearings. International Journal of Mathematics and Computers in Simulation, 10, 32–38. http://www.scopus.com/inward/record.url?eid=2-s2.0-84956645386&partnerID=MN8TOARS

   Paper
3. Savin, L., Majorov, S., & Kornaev, A. (2016). Hydrodynamic effects influence on lateral vibrations of rigid symmetric rotor with fluid-film bearings. Vibroengineering Procedia, 8, 322–327. http://www.scopus.com/inward/record.url?eid=2-s2.0-84995494473&partnerID=MN8TOARS

   Paper
4. Kornaeva, E., Kornaev, A., & Savin, L. (2016). Inertial Method of Viscosity Measurement of the Complex Rheology Medium. Procedia Engineering, 150, 626–634. https://doi.org/10.1016/j.proeng.2016.07.056

   Paper
5. Kornaeva, E., Savin, L., Kornaev, A., & Arkhipov, V. (2016). Mathematical model and finite-volume solution of a three-dimensional fluid flow between an eccentric cylinder and a cone. International Journal of Mathematical Models and Methods in Applied Sciences, 10, 87–93. http://www.scopus.com/inward/record.url?eid=2-s2.0-85000783888&partnerID=MN8TOARS

   Paper
6. Kornaeva, E., Kornaev, A., Savin, L., Galichev, A., & Babin, A. (2016). Theoretical premises of a vibro-inertial method of viscosity measurement. Vibroengineering Procedia, 8, 440–445. http://www.scopus.com/inward/record.url?eid=2-s2.0-84995426657&partnerID=MN8TOARS

   Paper

2015

1. Savin, L., Kornaev, A., & Kornaeva, E. (2015). Variational principle in the hydrodynamic lubrication theory. International Journal of Mathematical Models and Methods in Applied Sciences, 6, 8. http://www.scopus.com/inward/record.url?eid=2-s2.0-84926507849&partnerID=MN8TOARS

   Paper
2. КОРНАЕВ, А., МАШКОВ, Е., & МАШКОВ, А. (2015). К ВОПРОСУ ИСПОЛЬЗОВАНИЯ ФУНКЦИОНАЛА ЛАГРАНЖА В РЕШЕНИИ ЗАДАЧИ ГИДРОДИНАМИКИ. Ученые Записки Орловского Государственного Университета. Серия: Естественные, Технические и Медицинские Науки, (4), 29–31.

2014

1. Savin, L., Kornaev, A., & Kornaeva, E. (2014). Effect of Lubrication of Fluid Friction Bearings with Media of Complex Rheology. Applied Mechanics and Materials, 630, 199–207. https://doi.org/10.4028/www.scientific.net/amm.630.199

   Paper
2. Kornaev, A., Savin, L., & Nozdrichkin, M. (2014). The Application of Molecular Dynamics in Fullerene-Based Journal Bearing Simulation. Lubricants, 2(1), 1–10. https://doi.org/10.3390/lubricants2010001

   Paper
3. Корнаев, А. В., Чичварин, А. В., Смирнов, В. П., Корнаева, Е. П., Лебединский, В. И., & Ноздричкин, М. С. (2014). Влияние нанодобавок на трибологические и динамические качества смазки подшипников. Мир Транспорта и Технологических Машин, (4), 18–26.
4. Корнаев, А., Корнаева, Е., Машков, Е., & Лебединский, В. (2014). Применение метода контрольных объемов в решении задачи о сдвиговом течении жидкости сложной реологии. Известия Юго-Западного Государственного Университета, (5), 9.

2013

1. Корнаев, А., Савин, Л., Корнаева, Е., & Антонов, П. (2013). Инерционный способ измерения коэффициента динамической вязкости гибридной наномодифицированной жидкости. Известия Юго-Западного Государственного Университета, (1), 139–146.
2. Корнаев, А. В., Савин, Л. А., & Степанов, Ю. С. (2013). Роль нанотехнологий в развитии гидродинамической теории смазки. Фундаментальные и Прикладные Проблемы Техники и Технологии, 137, 69.

2012

1. Корнаев, А., Корнаева, Е., Майоров, С., & Савин, Л. (2012). Аналитическое обоснование возникновения центрирующей силы в несоосном канале цилиндр-конус при напорном течении вязкой жидкости. Известия Юго-Западного Государственного Университета, (5-2), 194–199.

2011

1. Корнаев, А. В., Савин, Л. А., & Герасимов, С. А. (2011). Фундаментальные вопросы проектирования свойств гидродинамической гибридной смазки. Фундаментальные и Прикладные Проблемы Техники и Технологии, (1), 46–51.