Artificial Intelligence in Physical Sciences: Symbolic Regression Trends and Perspectives

Symbolic regression (SR) is a machine learning-based regression method based on genetic programming principles that integrates techniques and processes from heterogeneous scientific fields and is capable of providing analytical equations purely from data. This remarkable characteristic diminishes th...

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Veröffentlicht in:Archives of computational methods in engineering Jg. 30; H. 6; S. 3845 - 3865
Hauptverfasser: Angelis, Dimitrios, Sofos, Filippos, Karakasidis, Theodoros E.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Dordrecht Springer Netherlands 01.07.2023
Springer Nature B.V
Schlagworte:
Algorithms
Approximation
Artificial intelligence
Big Data
Classification
Deep learning
Engineering
Genetic algorithms
Machine learning
Materials science
Mathematical and Computational Engineering
Neural networks
Partial differential equations
Physical properties
Physical sciences
Physics
Regression
Review
Review Article
Support vector machines
ISSN:1134-3060, 1886-1784, 1886-1784
Online-Zugang:Volltext
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Beschreibung
Zusammenfassung:Symbolic regression (SR) is a machine learning-based regression method based on genetic programming principles that integrates techniques and processes from heterogeneous scientific fields and is capable of providing analytical equations purely from data. This remarkable characteristic diminishes the need to incorporate prior knowledge about the investigated system. SR can spot profound and elucidate ambiguous relations that can be generalizable, applicable, explainable and span over most scientific, technological, economical, and social principles. In this review, current state of the art is documented, technical and physical characteristics of SR are presented, the available programming techniques are investigated, fields of application are explored, and future perspectives are discussed.
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ISSN:1134-3060
1886-1784
1886-1784
DOI:10.1007/s11831-023-09922-z