Angelis D, Sofos F, Karakasidis TE. Artificial Intelligence in Physical Sciences: Symbolic Regression Trends and Perspectives.
ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING : STATE OF THE ART REVIEWS 2023;
30:1-21. [PMID:
37359747 PMCID:
PMC10113133 DOI:
10.1007/s11831-023-09922-z]
[Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/04/2022] [Accepted: 03/27/2023] [Indexed: 06/28/2023]
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.
Supplementary Information
The online version contains supplementary material available at 10.1007/s11831-023-09922-z.
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