Artificial Intelligence in Physical Sciences: Symbolic Regression Trends and Perspectives

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@Article{Angelis:2023:ACME,

• author = "Dimitrios Angelis and Filippos Sofos and Theodoros E. Karakasidis",
• title = "Artificial Intelligence in Physical Sciences: Symbolic Regression Trends and Perspectives",
• journal = "Archives of Computational Methods in Engineering",
• year = "2023",
• volume = "30",
• pages = "3845--3865",
• month = jul,
• keywords = "genetic algorithms, genetic programming",
• URL = "https://rdcu.be/dmkPm",
• DOI = "doi:10.1007/s11831-023-09922-z",
• size = "21 pages",
• 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 generalisable, 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.",
• notes = "Condensed Matter Physics Laboratory, Department of Physics, University of Thessaly, Lamia 35100, Greece",

}

Genetic Programming entries for Dimitrios Angelis Filippos Sofos Theodoros E Karakasidis

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