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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| Vydáno v: | Archives of computational methods in engineering Ročník 30; číslo 6; s. 3845 - 3865 |
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| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Dordrecht
Springer Netherlands
01.07.2023
Springer Nature B.V |
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| ISSN: | 1134-3060, 1886-1784, 1886-1784 |
| On-line přístup: | Získat plný text |
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| Abstract | 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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| AbstractList | 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. 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. 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.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.The online version contains supplementary material available at 10.1007/s11831-023-09922-z.Supplementary InformationThe online version contains supplementary material available at 10.1007/s11831-023-09922-z. 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. The online version contains supplementary material available at 10.1007/s11831-023-09922-z. |
| Author | Sofos, Filippos Angelis, Dimitrios Karakasidis, Theodoros E. |
| Author_xml | – sequence: 1 givenname: Dimitrios surname: Angelis fullname: Angelis, Dimitrios organization: Condensed Matter Physics Laboratory, Department of Physics, University of Thessaly – sequence: 2 givenname: Filippos orcidid: 0000-0001-5036-2120 surname: Sofos fullname: Sofos, Filippos email: fsofos@uth.gr organization: Condensed Matter Physics Laboratory, Department of Physics, University of Thessaly – sequence: 3 givenname: Theodoros E. surname: Karakasidis fullname: Karakasidis, Theodoros E. organization: Condensed Matter Physics Laboratory, Department of Physics, University of Thessaly |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/37359747$$D View this record in MEDLINE/PubMed |
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| License | The Author(s) 2023. 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/. |
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| SubjectTerms | 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 |
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