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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:

Data-Driven Partial Derivative Equations Discovery with Evolutionary Approach
Mikhail Maslyaev, Alexander Hvatov, Anna V. Kalyuzhnaya
Lecture notes in computer science (2019), pp. 635-641
Closed Access | Times Cited: 32

Showing 1-25 of 32 citing articles:

Symbolic regression in materials science
Yiqun Wang, Nicholas Wagner, James M. Rondinelli
MRS Communications (2019) Vol. 9, Iss. 3, pp. 793-805
Open Access | Times Cited: 224
Promising directions of machine learning for partial differential equations
Steven L. Brunton, J. Nathan Kutz
Nature Computational Science (2024) Vol. 4, Iss. 7, pp. 483-494
Closed Access | Times Cited: 21
DLGA-PDE: Discovery of PDEs with incomplete candidate library via combination of deep learning and genetic algorithm
Hao Xu, Haibin Chang, Dongxiao Zhang
Journal of Computational Physics (2020) Vol. 418, pp. 109584-109584
Open Access | Times Cited: 87
Symbolic genetic algorithm for discovering open-form partial differential equations (SGA-PDE)
Yuntian Chen, Yingtao Luo, Qiang Liu, et al.
Physical Review Research (2022) Vol. 4, Iss. 2
Open Access | Times Cited: 53
Discovery of Partial Differential Equations from Highly Noisy and Sparse Data with Physics-Informed Information Criterion
Hao Xu, Junsheng Zeng, Dongxiao Zhang
Research (2023) Vol. 6
Open Access | Times Cited: 15
Deep-learning based discovery of partial differential equations in integral form from sparse and noisy data
Hao Xu, Dongxiao Zhang, Nanzhe Wang
Journal of Computational Physics (2021) Vol. 445, pp. 110592-110592
Open Access | Times Cited: 30
A Review of Data‐Driven Discovery for Dynamic Systems
Joshua S. North, Christopher K. Wikle, Erin M. Schliep
International Statistical Review (2023) Vol. 91, Iss. 3, pp. 464-492
Open Access | Times Cited: 12
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
Quantum Quantile Mechanics: Solving Stochastic Differential Equations for Generating Time‐Series
Annie E. Paine, Vincent E. Elfving, Oleksandr Kyriienko
Advanced Quantum Technologies (2023) Vol. 6, Iss. 10
Open Access | Times Cited: 10
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
Deep Learning-Meshless Method for Inverse Potential Problems
Jin Yan, Yumin Cheng
International Journal of Applied Mechanics (2024) Vol. 16, Iss. 08
Closed Access | Times Cited: 3
Bayesian learning of stochastic dynamical models
Peter Y. Lu, Pierre F. J. Lermusiaux
Physica D Nonlinear Phenomena (2021) Vol. 427, pp. 133003-133003
Open Access | Times Cited: 20
Deep-learning of parametric partial differential equations from sparse and noisy data
Hao Xu, Dongxiao Zhang, Junsheng Zeng
Physics of Fluids (2021) Vol. 33, Iss. 3
Open Access | Times Cited: 18
Data‐Driven Discovery of Soil Moisture Flow Governing Equation: A Sparse Regression Framework
Wenxiang Song, Liangsheng Shi, Lijun Wang, et al.
Water Resources Research (2022) Vol. 58, Iss. 8
Closed Access | Times Cited: 12
Towards Generative Design of Computationally Efficient Mathematical Models with Evolutionary Learning
Anna V. Kalyuzhnaya, Nikolay O. Nikitin, Alexander Hvatov, et al.
Entropy (2020) Vol. 23, Iss. 1, pp. 28-28
Open Access | Times Cited: 13
A robust framework for identification of PDEs from noisy data
Zhiming Zhang, Yongming Liu
Journal of Computational Physics (2021) Vol. 446, pp. 110657-110657
Open Access | Times Cited: 11
Data-driven discovery of governing equations for transient heat transfer analysis
Guodong Jin, Huilin Xing, Rongxin Zhang, et al.
Computational Geosciences (2022) Vol. 26, Iss. 3, pp. 613-631
Closed Access | Times Cited: 8
Visual Physics: Discovering Physical Laws from Videos
Pradyumna Chari, Chinmay Talegaonkar, Yunhao Ba, et al.
arXiv (Cornell University) (2019)
Open Access | Times Cited: 7
Data-Driven Partial Differential Equations Discovery Approach for the Noised Multi-dimensional Data
Mikhail Maslyaev, Alexander Hvatov, Anna V. Kalyuzhnaya
Lecture notes in computer science (2020), pp. 86-100
Open Access | Times Cited: 7
The data-driven physical-based equations discovery using evolutionary approach
Alexander Hvatov, Mikhail Maslyaev
Proceedings of the Genetic and Evolutionary Computation Conference Companion (2020), pp. 129-130
Open Access | Times Cited: 7
Sparsely constrained neural networks for model discovery of PDEs
Gert-Jan Both, Rémy Kusters
arXiv (Cornell University) (2020)
Closed Access | Times Cited: 6
Discovery of the data-driven models of continuous metocean process in form of nonlinear ordinary differential equations
Mikhail Maslyaev, Alexander Hvatov, Anna V. Kalyuzhnaya
Procedia Computer Science (2020) Vol. 178, pp. 18-26
Open Access | Times Cited: 3
Uncovering Closed-form Governing Equations of Nonlinear Dynamics from Videos
Lele Luan, Yang Liu, Hao Sun
arXiv (Cornell University) (2021)
Open Access | Times Cited: 3
A Random Fourier Feature Method for Emulating Computer Models With Gradient Information
Tzu-Hsiang Hung, Peter Chien
Technometrics (2020) Vol. 63, Iss. 4, pp. 500-509
Closed Access | Times Cited: 1
Deep-Learning Discovers Macroscopic Governing Equations for Viscous Gravity Currents from Microscopic Simulation Data
Junsheng Zeng, Hao Xu, Yuntian Chen, et al.
arXiv (Cornell University) (2021)
Open Access | Times Cited: 1
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