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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Detalles Bibliográficos
Autores principales: Angelis, Dimitrios, Sofos, Filippos, Karakasidis, Theodoros E.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Netherlands 2023
Materias:
Review Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10113133/
https://www.ncbi.nlm.nih.gov/pubmed/37359747
http://dx.doi.org/10.1007/s11831-023-09922-z
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author Angelis, Dimitrios
Sofos, Filippos
Karakasidis, Theodoros E.
author_facet Angelis, Dimitrios
Sofos, Filippos
Karakasidis, Theodoros E.
author_sort Angelis, Dimitrios
collection PubMed
description 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11831-023-09922-z.
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spelling pubmed-101131332023-04-20 Artificial Intelligence in Physical Sciences: Symbolic Regression Trends and Perspectives Angelis, Dimitrios Sofos, Filippos Karakasidis, Theodoros E. Arch Comput Methods Eng Review Article 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11831-023-09922-z. Springer Netherlands 2023-04-19 /pmc/articles/PMC10113133/ /pubmed/37359747 http://dx.doi.org/10.1007/s11831-023-09922-z Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Review Article
Angelis, Dimitrios
Sofos, Filippos
Karakasidis, Theodoros E.
Artificial Intelligence in Physical Sciences: Symbolic Regression Trends and Perspectives
title Artificial Intelligence in Physical Sciences: Symbolic Regression Trends and Perspectives
title_full Artificial Intelligence in Physical Sciences: Symbolic Regression Trends and Perspectives
title_fullStr Artificial Intelligence in Physical Sciences: Symbolic Regression Trends and Perspectives
title_full_unstemmed Artificial Intelligence in Physical Sciences: Symbolic Regression Trends and Perspectives
title_short Artificial Intelligence in Physical Sciences: Symbolic Regression Trends and Perspectives
title_sort artificial intelligence in physical sciences: symbolic regression trends and perspectives
topic Review Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10113133/
https://www.ncbi.nlm.nih.gov/pubmed/37359747
http://dx.doi.org/10.1007/s11831-023-09922-z
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