Shape-constrained Symbolic Regression: Real-World Applications in Magnetization, Extrusion and Data Validation

Christian Haider; Fabricio Olivetti de Franca; Bogdan Burlacu; Florian Bachinger; Gabriel Kronberger; Michael Affenzeller

doi:10.1007/978-981-99-8413-8_12

Shape-constrained Symbolic Regression: Real-World Applications in Magnetization, Extrusion and Data Validation

Christian Haider, Fabricio Olivetti de Franca, Bogdan Burlacu, Florian Bachinger, Gabriel Kronberger, Michael Affenzeller

Publikation: Beitrag in Buch/Bericht/Tagungsband › Kapitel › Begutachtung

Abstract

We present different approaches for including knowledge in data-based modeling. For this, we utilize the model representation of symbolic regression (SR), which represents the models as short interpretable mathematical formulas. The integration of knowledge into symbolic regressionSymbolic regression via shape constraints is discussed alongside three real-world applications: modeling magnetization curves, modeling twin-screw extruders and model-based data validation.

Originalsprache	undefiniert/unbekannt
Titel	Genetic Programming Theory and Practice XX
Redakteure/-innen	Stephan Winkler, Leonardo Trujillo, Charles Ofria, Ting Hu
Erscheinungsort	Singapore
Herausgeber (Verlag)	Springer Nature Singapore
Seiten	225-240
Seitenumfang	16
ISBN (Print)	978-981-99-8413-8
DOIs	https://doi.org/10.1007/978-981-99-8413-8_12
Publikationsstatus	Veröffentlicht - 2024

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10.1007/978-981-99-8413-8_12

Zitieren

Haider, C., de Franca, F. O., Burlacu, B., Bachinger, F., Kronberger, G., & Affenzeller, M. (2024). Shape-constrained Symbolic Regression: Real-World Applications in Magnetization, Extrusion and Data Validation. in S. Winkler, L. Trujillo, C. Ofria, & T. Hu (Hrsg.), Genetic Programming Theory and Practice XX (S. 225-240). Springer Nature Singapore. https://doi.org/10.1007/978-981-99-8413-8_12

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abstract = "We present different approaches for including knowledge in data-based modeling. For this, we utilize the model representation of symbolic regression (SR), which represents the models as short interpretable mathematical formulas. The integration of knowledge into symbolic regressionSymbolic regression via shape constraints is discussed alongside three real-world applications: modeling magnetization curves, modeling twin-screw extruders and model-based data validation.",

author = "Christian Haider and {de Franca}, {Fabricio Olivetti} and Bogdan Burlacu and Florian Bachinger and Gabriel Kronberger and Michael Affenzeller",

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Haider, C, de Franca, FO, Burlacu, B , Bachinger, F , Kronberger, G & Affenzeller, M 2024, Shape-constrained Symbolic Regression: Real-World Applications in Magnetization, Extrusion and Data Validation. in S Winkler, L Trujillo, C Ofria & T Hu (Hrsg.), Genetic Programming Theory and Practice XX. Springer Nature Singapore, Singapore, S. 225-240. https://doi.org/10.1007/978-981-99-8413-8_12

Shape-constrained Symbolic Regression: Real-World Applications in Magnetization, Extrusion and Data Validation. / Haider, Christian; de Franca, Fabricio Olivetti; Burlacu, Bogdan et al.
Genetic Programming Theory and Practice XX. Hrsg. / Stephan Winkler; Leonardo Trujillo; Charles Ofria; Ting Hu. Singapore: Springer Nature Singapore, 2024. S. 225-240.

Publikation: Beitrag in Buch/Bericht/Tagungsband › Kapitel › Begutachtung

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