Machine learning in physics: A short guide

Instituto de Ciências Matemáticas e de Computação, Universidade de São Paulo - São, Carlos, São Paulo, Brazil

^(a) E-mail: francisco@icmc.usp.br (corresponding author)

Received: 31 August 2023
Accepted: 20 October 2023

Abstract

Machine learning is a rapidly growing field with the potential to revolutionize many areas of science, including physics. This review provides a brief overview of machine learning in physics, covering the main concepts of supervised, unsupervised, and reinforcement learning, as well as more specialized topics such as causal inference, symbolic regression, and deep learning. We present some of the principal applications of machine learning in physics and discuss the associated challenges and perspectives.