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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:

Robust discovery of partial differential equations in complex situations
Hao Xu, Dongxiao Zhang
Physical Review Research (2021) Vol. 3, Iss. 3
Open Access | Times Cited: 23

Showing 23 citing articles:

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
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
Noise-aware physics-informed machine learning for robust PDE discovery
Pongpisit Thanasutives, Takashi Morita, Masayuki Numao, et al.
Machine Learning Science and Technology (2023) Vol. 4, Iss. 1, pp. 015009-015009
Open Access | Times Cited: 14
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
Interference Model Guided Neural Network for Aeromagnetic Compensation
Yujing Xu, Zhongyan Liu, Qi Zhang, et al.
IEEE Sensors Journal (2024) Vol. 24, Iss. 8, pp. 12266-12275
Closed Access | Times Cited: 3
An invariance constrained deep learning network for partial differential equation discovery
C. S. Chen, Hui Li, Xiaowei Jin
Physics of Fluids (2024) Vol. 36, Iss. 4
Open Access | Times Cited: 2
An Improved PINN Algorithm for Shallow Water Equations Driven by Deep Learning
Yanling Li, Qianxing Sun, Junfang Wei, et al.
Symmetry (2024) Vol. 16, Iss. 10, pp. 1376-1376
Open Access | Times Cited: 2
Reconstructing the Unsaturated Flow Equation From Sparse and Noisy Data: Leveraging the Synergy of Group Sparsity and Physics‐Informed Deep Learning
Wenxiang Song, Liangsheng Shi, Xiaolong Hu, et al.
Water Resources Research (2023) Vol. 59, Iss. 5
Closed Access | Times Cited: 6
Deep learning discovery of macroscopic governing equations for viscous gravity currents from microscopic simulation data
Junsheng Zeng, Hao Xu, Yuntian Chen, et al.
Computational Geosciences (2023) Vol. 27, Iss. 6, pp. 987-1000
Open Access | Times Cited: 5
Discover governing differential equations from evolving systems
Yuanyuan Li, Kai Wu, Jing Liu
Physical Review Research (2023) Vol. 5, Iss. 2
Open Access | Times Cited: 4
Automating the Discovery of Partial Differential Equations in Dynamical Systems
Weizhen Li, Rui Carvalho
Machine Learning Science and Technology (2024) Vol. 5, Iss. 3, pp. 035046-035046
Open Access | Times Cited: 1
Deep learning‐based method for solving seepage equation under unsteady boundary
Daolun Li, Luhang Shen, Wenshu Zha, et al.
International Journal for Numerical Methods in Fluids (2023) Vol. 96, Iss. 1, pp. 87-101
Closed Access | Times Cited: 3
Deep-OSG: Deep learning of operators in semigroup
Junfeng Chen, Kailiang Wu
Journal of Computational Physics (2023) Vol. 493, pp. 112498-112498
Open Access | Times Cited: 1
Identification of partial differential equations from noisy data with integrated knowledge discovery and embedding using evolutionary neural networks
Hanyu Zhou, Haochen Li, Yaomin Zhao
Theoretical and Applied Mechanics Letters (2024) Vol. 14, Iss. 2, pp. 100511-100511
Open Access
Discovering an interpretable mathematical expression for a full wind-turbine wake with artificial intelligence enhanced symbolic regression
Ding Wang, Yuntian Chen, Shiyi Chen
Physics of Fluids (2024) Vol. 36, Iss. 10
Open Access
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
Sparsistent model discovery
Georges Tod, Gert-Jan Both, Rémy Kusters
HAL (Le Centre pour la Communication Scientifique Directe) (2021)
Open Access | Times Cited: 1
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
Evolutionary Machine Learning in Science and Engineering
Jianjun Hu, Yuqi Song, Sadman Sadeed Omee, et al.
Genetic and evolutionary computation (2023), pp. 535-561
Closed Access
Critical Sampling for Robust Evolution Operator Learning of Unknown Dynamical Systems
Ce Zhang, Kailiang Wu, Zhihai He
IEEE Transactions on Artificial Intelligence (2023) Vol. 5, Iss. 6, pp. 2856-2871
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
Discovery of partial differential equations from highly noisy and sparse data with physics-informed information criterion
Hao Xu, Junsheng Zeng, Dongxiao Zhang
arXiv (Cornell University) (2022)
Open Access
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