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Artificial Intelligence in Physical Sciences: Symbolic Regression Trends and Perspectives
Dimitrios Angelis1, Filippos Sofos1, Theodoros E Karakasidis1
1Condensed Matter Physics Laboratory, Department of Physics, University of Thessaly, Lamia, 35100 Greece.
Archives of Computational Methods in Engineering : State of the Art Reviews
|June 26, 2023
Summary
Symbolic regression (SR) is a powerful machine learning technique that discovers analytical equations directly from data, eliminating the need for prior system knowledge. This review covers SR
Area of Science:
- Machine Learning
- Data Science
- Computational Science
Background:
- Symbolic Regression (SR) is a data-driven machine learning approach.
- It leverages genetic programming principles and integrates diverse scientific techniques.
- SR can uncover complex, generalizable, and explainable relationships without prior system knowledge.
Purpose of the Study:
- To provide a comprehensive review of the current state-of-the-art in Symbolic Regression.
- To detail the technical and physical characteristics of SR methodologies.
- To explore available programming techniques, application domains, and future research directions.
Main Methods:
- Review of existing literature on Symbolic Regression.
- Analysis of SR's technical and physical attributes.
- Investigation of various programming approaches and their applications.
Main Results:
- SR can generate analytical equations directly from data.
- It reduces the necessity for pre-existing domain expertise.
- SR identifies profound and ambiguous relationships across scientific, technological, economic, and social fields.
Conclusions:
- Symbolic Regression offers a powerful, knowledge-free approach to scientific discovery.
- Its ability to derive explainable equations from data makes it broadly applicable.
- The field shows significant promise for future advancements and wider adoption.
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