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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Partial differential equations discovery with EPDE framework: Application for real and synthetic data
Mikhail Maslyaev, Alexander Hvatov, Anna V. Kalyuzhnaya
Journal of Computational Science (2021) Vol. 53, pp. 101345-101345
Closed Access | Times Cited: 26

Showing 1-25 of 26 citing articles:

DISCOVER: Deep identification of symbolically concise open-form partial differential equations via enhanced reinforcement learning
Mengge Du, Yuntian Chen, Dongxiao Zhang
Physical Review Research (2024) Vol. 6, Iss. 1
Open Access | Times Cited: 13
Physics-constrained robust learning of open-form partial differential equations from limited and noisy data
Mengge Du, Yuntian Chen, Longfeng Nie, et al.
Physics of Fluids (2024) Vol. 36, Iss. 5
Open Access | Times Cited: 5
A study of mechanism-data hybrid-driven method for multibody system via physics-informed neural network
Ningning Song, Chuanda Wang, Haijun Peng, et al.
Acta Mechanica Sinica (2024) Vol. 41, Iss. 3
Closed Access | Times Cited: 4
Differential Equation Discovery of Robotic Swarm as Active Matter
Roman Titov, Alexander Hvatov
Lecture notes in computer science (2025), pp. 213-228
Closed Access
Knowledge-aware differential equation discovery with automated background knowledge extraction
Elizaveta Ivanchik, Alexander Hvatov
Information Sciences (2025), pp. 122131-122131
Closed Access
Deep recognition of partial differential equations based on reinforcement learning and genetic algorithm
Jinyang Du, Renyun Liu, Du Cheng, et al.
The Journal of Supercomputing (2025) Vol. 81, Iss. 5
Closed Access
Learning dynamics from coarse/noisy data with scalable symbolic regression
Zhao Chen, Nan Wang
Mechanical Systems and Signal Processing (2023) Vol. 190, pp. 110147-110147
Closed Access | Times Cited: 7
Automated Differential Equation Solver Based on the Parametric Approximation Optimization
Alexander Hvatov
Mathematics (2023) Vol. 11, Iss. 8, pp. 1787-1787
Open Access | Times Cited: 4
Solver-Based Fitness Function for the Data-Driven Evolutionary Discovery of Partial Differential Equations
Mikhail Maslyaev, Alexander Hvatov
2022 IEEE Congress on Evolutionary Computation (CEC) (2022), pp. 1-8
Closed Access | Times Cited: 7
MORL4PDEs: Data-driven discovery of PDEs based on multi-objective optimization and reinforcement learning
Xiaoxia Zhang, Junsheng Guan, Yanjun Liu, et al.
Chaos Solitons & Fractals (2024) Vol. 180, pp. 114536-114536
Closed Access | Times Cited: 1
A method for reconstructing models of heat and mass transfer from the spatio-temporal distribution of parameters
N. Y. Bykov, Alexander Hvatov, Anna V. Kalyuzhnaya, et al.
Письма в журнал технической физики (2022) Vol. 48, Iss. 15, pp. 50-50
Open Access | Times Cited: 4
Directed Differential Equation Discovery Using Modified Mutation and Cross-Over Operators
Elizaveta Ivanchik, Alexander Hvatov
2022 IEEE Congress on Evolutionary Computation (CEC) (2023), pp. 1-10
Open Access | Times Cited: 2
20 years of computational science: Selected papers from 2020 International Conference on Computational Science
Sergey V. Kovalchuk, Valeria V. Krzhizhanovskaya, Maciej Paszyński, et al.
Journal of Computational Science (2021) Vol. 53, pp. 101395-101395
Closed Access | Times Cited: 4
Parameter Estimation for Several Types of Linear Partial Differential Equations Based on Gaussian Processes
Wenbo Zhang, Wei Gu
Fractal and Fractional (2022) Vol. 6, Iss. 8, pp. 433-433
Open Access | Times Cited: 3
Precision Calorimeter Model Development: Generative Design Approach
T. A. Andreeva, Nikolay Bykov, T. A. Kompan, et al.
Processes (2023) Vol. 11, Iss. 1, pp. 152-152
Open Access | Times Cited: 1
Numerical Investigation of a Class of Nonlinear Time-Dependent Delay PDEs Based on Gaussian Process Regression
Wei Gu, Wenbo Zhang, Yaling Han
Fractal and Fractional (2022) Vol. 6, Iss. 10, pp. 606-606
Open Access | Times Cited: 1
Model-Agnostic Multi-objective Approach for the Evolutionary Discovery of Mathematical Models
Alexander Hvatov, Mikhail Maslyaev, Iana S. Polonskaya, et al.
Communications in computer and information science (2021), pp. 72-85
Closed Access | Times Cited: 1
Метод восстановления моделей тепломассопереноса по пространственно-временным распределениям параметров
Н.Ю. Быков, А.А. Хватов, А.В. Калюжная, et al.
Письма в журнал технической физики (2021) Vol. 47, Iss. 24, pp. 9-9
Open Access | Times Cited: 1
Multiobjective evolutionary discovery of equation-based analytical models for dynamical systems
Mikhail Maslyaev, Alexander Hvatov
Scientific and technical journal of information technologies mechanics and optics (2023) Vol. 23, Iss. 1, pp. 97-104
Open Access
On a Structure of an Automated Differential Equation Solver Based on Machine Learning Methods
Damir Aminev, Nikita Demyanchuk, Alexander Hvatov
Lecture notes in networks and systems (2023), pp. 438-447
Closed Access
Methods of Partial Differential Equation Discovery: Application to Experimental Data on Heat Transfer Problem
T. A. Andreeva, N. Y. Bykov, Y A Gataulin, et al.
Processes (2023) Vol. 11, Iss. 9, pp. 2719-2719
Open Access
Equation Discovery Framework Epde: Towards a Better Equation Discovery
Mikhail Maslyaev, Alexander Hvatov
(2023)
Closed Access
Forecasting of Sea Ice Concentration using CNN, PDE discovery and Bayesian Networks
Julia Borisova, Roman Titov, Karine Shakhkyan, et al.
Procedia Computer Science (2023) Vol. 229, pp. 177-187
Open Access
Methods for a Partial Differential Equation Discovery: Application to Physical and Engineering Problems
N. Y. Bykov, A. А. Hvatov, T. A. Andreeva, et al.
Moscow University Physics Bulletin (2023) Vol. 78, Iss. S1, pp. S256-S265
Closed Access
Towards Discovery of the Differential Equations
A. А. Hvatov, Roman Titov
Doklady Mathematics (2023) Vol. 108, Iss. S2, pp. S257-S264
Closed Access
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