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Exploring multi-view symbolic regression methods in physical sciences.

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Summary
This summary is machine-generated.

This study compares multi-view symbolic regression (MvSR) tools for discovering interpretable mathematical models from data. While all tools show accuracy, specific features enhance the generation of superior, parsimonious equations.

Keywords:
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Area of Science:

  • * Computational Physics
  • * Scientific Machine Learning
  • * Data Science

Background:

  • * Mathematical functions are crucial for understanding natural phenomena.
  • * Symbolic Regression (SR) automates the discovery of interpretable equations from data.
  • * Multi-view Symbolic Regression (MvSR) extends SR to leverage multiple datasets, reducing overfitting and data scarcity.

Purpose of the Study:

  • * To benchmark and compare the performance of different MvSR implementations.
  • * To identify features that contribute to the generation of high-quality, parsimonious models.
  • * To provide recommendations for future MvSR algorithm development.

Main Methods:

  • * Evaluation of MvSR algorithms (Operon, PySR, ϕ-SO, eggp) on diverse real-world datasets.
  • * Comparative analysis of model accuracy, interpretability, and parameter efficiency.
  • * Identification of key algorithmic features influencing model performance.

Main Results:

  • * All tested MvSR implementations frequently achieve good accuracy on real-world data.
  • * Models generated often feature a small number of parameters, enhancing interpretability.
  • * Certain algorithmic features were found to significantly improve the quality of discovered models.

Conclusions:

  • * MvSR is a powerful technique for discovering scientific equations from multiple datasets.
  • * Algorithmic design choices significantly impact the effectiveness of MvSR tools.
  • * Guidelines are proposed to steer future advancements in MvSR for enhanced scientific discovery.