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:
Toward Physically Plausible Data-Driven Models: A Novel Neural Network Approach to Symbolic Regression
Jiřı́ Kubalı́k, Erik Derner, Robert Babuška
IEEE Access (2023) Vol. 11, pp. 61481-61501
Open Access | Times Cited: 7
Jiřı́ Kubalı́k, Erik Derner, Robert Babuška
IEEE Access (2023) Vol. 11, pp. 61481-61501
Open Access | Times Cited: 7
Showing 7 citing articles:
SymbolNet: Neural symbolic regression with adaptive dynamic pruning for compression
Ho Fung Tsoi, Vladimir Lončar, Sridhara Dasu, et al.
Machine Learning Science and Technology (2025) Vol. 6, Iss. 1, pp. 015021-015021
Open Access | Times Cited: 1
Ho Fung Tsoi, Vladimir Lončar, Sridhara Dasu, et al.
Machine Learning Science and Technology (2025) Vol. 6, Iss. 1, pp. 015021-015021
Open Access | Times Cited: 1
Training stiff neural ordinary differential equations with explicit exponential integration methods
Colby Fronk, Linda Petzold
Chaos An Interdisciplinary Journal of Nonlinear Science (2025) Vol. 35, Iss. 3
Closed Access
Colby Fronk, Linda Petzold
Chaos An Interdisciplinary Journal of Nonlinear Science (2025) Vol. 35, Iss. 3
Closed Access
Using machine learning to find exact analytic solutions to analytically posed physics problems
Sahel Ashhab
Heliyon (2024) Vol. 10, Iss. 6, pp. e28124-e28124
Open Access | Times Cited: 3
Sahel Ashhab
Heliyon (2024) Vol. 10, Iss. 6, pp. e28124-e28124
Open Access | Times Cited: 3
Harnessing data using symbolic regression methods for discovering novel paradigms in physics
Jianyang Guo, Wan‐Jian Yin
Science China Physics Mechanics and Astronomy (2024) Vol. 67, Iss. 6
Closed Access | Times Cited: 2
Jianyang Guo, Wan‐Jian Yin
Science China Physics Mechanics and Astronomy (2024) Vol. 67, Iss. 6
Closed Access | Times Cited: 2
Bayesian polynomial neural networks and polynomial neural ordinary differential equations
Colby Fronk, Jae-Woong Yun, Prashant Singh, et al.
PLoS Computational Biology (2024) Vol. 20, Iss. 10, pp. e1012414-e1012414
Open Access | Times Cited: 2
Colby Fronk, Jae-Woong Yun, Prashant Singh, et al.
PLoS Computational Biology (2024) Vol. 20, Iss. 10, pp. e1012414-e1012414
Open Access | Times Cited: 2
Training stiff neural ordinary differential equations with implicit single-step methods
Colby Fronk, Linda Petzold
Chaos An Interdisciplinary Journal of Nonlinear Science (2024) Vol. 34, Iss. 12
Open Access | Times Cited: 1
Colby Fronk, Linda Petzold
Chaos An Interdisciplinary Journal of Nonlinear Science (2024) Vol. 34, Iss. 12
Open Access | Times Cited: 1
Exploring the mathematic equations behind the materials science data using interpretable symbolic regression
Guanjie Wang, Erpeng Wang, Zefeng Li, et al.
Interdisciplinary materials (2024) Vol. 3, Iss. 5, pp. 637-657
Open Access
Guanjie Wang, Erpeng Wang, Zefeng Li, et al.
Interdisciplinary materials (2024) Vol. 3, Iss. 5, pp. 637-657
Open Access
Using machine learning to find exact analytic solutions to analytically posed physics problems
Sahel Ashhab
arXiv (Cornell University) (2023)
Open Access
Sahel Ashhab
arXiv (Cornell University) (2023)
Open Access
