Symbolic Regression (SR) is a type of regression analysis used to find the model which can best fit a dataset according to a specified condition by conducting a search on the space of mathematical expressions. This talk aims to give an introduction to this topic, to succinctly present a few popular and particularly Physics-oriented search algorithms, and to showcase examples of SR’s application in research.

Organized by: Tuhin Malik