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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:

Shape-Constrained Symbolic Regression—Improving Extrapolation with Prior Knowledge
Gabriel Kronberger, Fabrício Olivetti de França, Bogdan Burlacu, et al.
Evolutionary Computation (2021) Vol. 30, Iss. 1, pp. 75-98
Open Access | Times Cited: 39

Showing 1-25 of 39 citing articles:

Artificial Intelligence in Physical Sciences: Symbolic Regression Trends and Perspectives
Dimitrios Angelis, Filippos Sofos, Theodoros E. Karakasidis
Archives of Computational Methods in Engineering (2023) Vol. 30, Iss. 6, pp. 3845-3865
Open Access | Times Cited: 72
A computational framework for physics-informed symbolic regression with straightforward integration of domain knowledge
Liron Simon Keren, Alex Liberzon, Teddy Lazebnik
Scientific Reports (2023) Vol. 13, Iss. 1
Open Access | Times Cited: 65
Discovering causal relations and equations from data
Gustau Camps‐Valls, Andreas Gerhardus, Urmi Ninad, et al.
Physics Reports (2023) Vol. 1044, pp. 1-68
Open Access | Times Cited: 42
Symbolic regression as a feature engineering method for machine and deep learning regression tasks
Assaf Shmuel, Oren Glickman, Teddy Lazebnik
Machine Learning Science and Technology (2024) Vol. 5, Iss. 2, pp. 025065-025065
Open Access | Times Cited: 5
Shape-constrained multi-objective genetic programming for symbolic regression
Christian Haider, Fabrício Olivetti de França, Bogdan Burlacu, et al.
Applied Soft Computing (2022) Vol. 132, pp. 109855-109855
Open Access | Times Cited: 16
Theory-inspired machine learning—towards a synergy between knowledge and data
Johannes G. Hoffer, Andreas B. Ofner, Franz M. Rohrhofer, et al.
Welding in the World (2022) Vol. 66, Iss. 7, pp. 1291-1304
Open Access | Times Cited: 15
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
Evolutionary neural networks for learning turbulence closure models with explicit expressions
Haochen Li, Yaomin Zhao, Fabian Waschkowski, et al.
Physics of Fluids (2024) Vol. 36, Iss. 5
Closed Access | Times Cited: 2
Integrating knowledge-guided symbolic regression and model-based design of experiments to automate process flow diagram development
Alexander W. Rogers, Amanda Lane, Ćesar Mendoza, et al.
Chemical Engineering Science (2024) Vol. 300, pp. 120580-120580
Open Access | Times Cited: 2
A mathematical framework of SMS reminder campaigns for pre- and post-diagnosis check-ups using socio-demographics: An in-silco investigation into breast cancer
Elizaveta Savchenko, Ariel Rosenfeld, Svetlana Bunimovich‐Mendrazitsky
Socio-Economic Planning Sciences (2024) Vol. 95, pp. 102047-102047
Closed Access | Times Cited: 2
Complementing a continuum thermodynamic approach to constitutive modeling with symbolic regression
Karl Garbrecht, Donovan Birky, Brian Lester, et al.
Journal of the Mechanics and Physics of Solids (2023) Vol. 181, pp. 105472-105472
Closed Access | Times Cited: 5
Alleviating overfitting in transformation-interaction-rational symbolic regression with multi-objective optimization
Fabrício Olivetti de França
Genetic Programming and Evolvable Machines (2023) Vol. 24, Iss. 2
Open Access | Times Cited: 4
Multi-gene genetic programming extension of AASHTO M-E for design of low-volume concrete pavements
Haoran Li, Lev Khazanovich
Journal of Road Engineering (2022) Vol. 2, Iss. 3, pp. 252-266
Open Access | Times Cited: 7
Comparing optimistic and pessimistic constraint evaluation in shape-constrained symbolic regression
Christian Haider, Fabrício Olivetti de França, Gabriel Kronberger, et al.
Proceedings of the Genetic and Evolutionary Computation Conference (2022), pp. 938-945
Open Access | Times Cited: 3
Physics-Informed Research Assistant for Theory Extraction (PIRATE) for Missing Physics Discovery
Lele Luan, Ryan Jacobs, Sayan Ghosh, et al.
AIAA SCITECH 2022 Forum (2024)
Closed Access
Automated Machine Learning for Industrial Applications – Challenges and Opportunities
Florian Bachinger, Jan Zenisek, Michael Affenzeller
Procedia Computer Science (2024) Vol. 232, pp. 1701-1710
Open Access
Shape-constrained Symbolic Regression: Real-World Applications in Magnetization, Extrusion and Data Validation
Christian Haider, Fabrício Olivetti de França, Bogdan Burlacu, et al.
Genetic and evolutionary computation (2024), pp. 225-240
Closed Access
Incorporating Background Knowledge in Symbolic Regression Using a Computer Algebra System
Charles W. Fox, Neil D. Tran, F Nikki Nacion, et al.
Machine Learning Science and Technology (2024) Vol. 5, Iss. 2, pp. 025057-025057
Open Access
Data Validation Utilizing Expert Knowledge and Shape Constraints
Florian Bachinger, Lisa Ehrlinger, Gabriel Kronberger, et al.
Journal of Data and Information Quality (2024) Vol. 16, Iss. 2, pp. 1-27
Open Access
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
Study α decay and proton emission based on data-driven symbolic regression
Jun-Hao Cheng, Binglin Wang, Wenyu Zhang, et al.
Computer Physics Communications (2024) Vol. 304, pp. 109317-109317
Closed Access
Safe Exploration in Reinforcement Learning by Reachability Analysis over Learned Models
Yuning Wang, He Zhu
Lecture notes in computer science (2024), pp. 232-255
Closed Access
Modeling plasticity-mediated void growth at the single crystal scale: A physics-informed machine learning approach
Karl Garbrecht, Andrea Rovinelli, Jacob Hochhalter, et al.
Mechanics of Materials (2024) Vol. 199, pp. 105151-105151
Closed Access
Research on Leaf Area Index Inversion Based on LESS 3D Radiative Transfer Model and Machine Learning Algorithms
Yunyang Jiang, Zixuan Zhang, Huaijiang He, et al.
Remote Sensing (2024) Vol. 16, Iss. 19, pp. 3627-3627
Open Access
Integrating small data and shape prior knowledge with gradient-enhanced Kriging through adaptive knowledge sampling
Hui Jun Long, Jia Hao, Wenbin Ye, et al.
Computers & Industrial Engineering (2024), pp. 110660-110660
Closed Access
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