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Artificial Intelligence in Physical Sciences: Symbolic Regression Trends and Perspectives.

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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.