Bykov N., Hvatov A., Andreeva T., Lukin A.Y., Maslyaev M., Obraztsov N.V., Surov A.V., Boukhanovsky A.V. . Methods for a Partial Differential Equation Discovery: Application to Physical and Engineering Problems. Moscow University Physics Bulletin. 2023. Vol. 78. No. Suppl.1. pp. S256-S265.
Maslyaev M., Hvatov A. Comparison of Single- and Multi- Objective Optimization Quality for Evolutionary Equation Discovery. GECCO 2023 - Proceedings of the Genetic and Evolutionary Computation Conference Companion. 2023. pp. 603–606.
Maslyaev M., Hvatov A. Multiobjective evolutionary discovery of equation-based analytical models for dynamical systems. Научно-технический вестник информационных технологий, механики и оптики [Scientific and Technical Journal of Information Technologies, Mechanics and Optics]. 2023. Vol. 23. No. 1(143). pp. 97-104.
Maslyaev M., Hvatov A. Solver-Based Fitness Function for the Data-Driven Evolutionary Discovery of Partial Differential Equations. IEEE Congress on Evolutionary Computation, CEC 2022. 2022. pp. 1-8.
Bykov N., Obraztsov N.V., Hvatov A., Maslyaev M., Surov A.V. Hybrid modeling of gas-dynamic processes in AC plasma torches. Физика и механика материалов = Materials Physics and Mechanics. 2022. Vol. 50. No. 2. pp. 287-303.
Maslyaev M., Hvatov A., Kalyuzhnaya A.V. Partial differential equations discovery with EPDE framework: Application for real and synthetic data (R). Journal of Computational Science. 2021. Vol. 53. pp. 101345.
Kalyuzhnaya A.V., Nikitin N.O., Hvatov A., Maslyaev M., Yachmenkov M., Boukhanovsky A.V. Towards generative design of computationally efficient mathematical models with evolutionary learning. Entropy. 2021. Vol. 23. No. 1. pp. 28.
Hvatov A., Maslyaev M., Polonskaia I.S., Sarafanov M.I., Merezhnikov M., Nikitin N.O. Model-Agnostic Multi-objective Approach for the Evolutionary Discovery of Mathematical Models. Communications in Computer and Information Science. 2021. Vol. 1488. pp. 72-85.
Maslyaev M., Hvatov A. Multi-Objective Discovery of PDE Systems Using Evolutionary Approach. IEEE Congress on Evolutionary Computation, CEC 2021. 2021. pp. 596-603.
Sarafanov M.I., Borisova Y., Maslyaev M., Revin I., Maximov G., Nikitin N.O. Short-Term River Flood Forecasting Using Composite Models and Automated Machine Learning: The Case Study of Lena River. Water. 2021. Vol. 13. No. 24. pp. 3482.
Maslyaev M., Hvatov A., Kalyuzhnaya A.V. Discovery of the data-driven models of continuous metocean process in form of nonlinear ordinary differential equations. Procedia Computer Science. 2020. Vol. 178. pp. 18-26.
Maslyaev M., Hvatov A., Kalyuzhnaya A. Data-Driven Partial Differential Equations Discovery Approach for the Noised Multi-dimensional Data. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2020. Vol. 12138 LNCS. pp. 86-100.
Hvatov A., Maslyaev M. The data-driven physical-based equations discovery using evolutionary approach. GECCO 2020 - Proceedings of the Genetic and Evolutionary Computation Conference Companion. 2020. pp. 129–130.
Maslyaev M., Hvatov A., Kalyuzhnaya A.V. Data-driven partial derivative equations discovery with evolutionary approach. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2019. Vol. 11540 LNCS. pp. 635-641.
Maslyaev M., Hvatov A. Discovery of the data-driven differential equation-based models of continuous metocean process. Procedia Computer Science. 2019. Vol. 156. pp. 367-376.
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