Workshop 2: An Introduction to Symbolic Regression with PySR and SymbolicRegression.jl

Speaker: Miles Cranmer, Princeton

Abstract: PySR (https://github.com/MilesCranmer/PySR) is an open-source library for practical symbolic regression, a type of machine learning that discovers human-interpretable symbolic models in the form of simple mathematical expressions. PySR is built on a high-performance distributed backend, SymbolicRegression.jl, which offers a flexible search algorithm, and interfaces with several deep learning packages. In this tutorial I will describe the nuts and bolts of the search algorithm and how PySR may be used in machine learning and scientific workflows. I will review existing applications of the software to science (https://astroautomata.com/PySR/papers/), and then present an interactive coding tutorial where we will go through several example symbolic regression problems with different levels of customization. Following this, we will look at using PySR as a distillation tool for translating deep neural networks into an interpretable scientific language, and go through additional examples.