# Machine learning and the physical sciences

## TL;DR

This paper reviews recent advances at the intersection of machine learning and physical sciences, highlighting conceptual developments, applications across various physics domains, and new computing architectures.

## Contribution

It provides a comprehensive overview of how machine learning techniques are being integrated into physical sciences, including conceptual insights, practical applications, and hardware innovations.

## Key findings

- ML methods are used to understand physical systems.
- Applications span particle physics, cosmology, quantum physics, and materials science.
- Emerging computing architectures accelerate ML in physics.

## Abstract

Machine learning encompasses a broad range of algorithms and modeling tools used for a vast array of data processing tasks, which has entered most scientific disciplines in recent years. We review in a selective way the recent research on the interface between machine learning and physical sciences. This includes conceptual developments in machine learning (ML) motivated by physical insights, applications of machine learning techniques to several domains in physics, and cross-fertilization between the two fields. After giving basic notion of machine learning methods and principles, we describe examples of how statistical physics is used to understand methods in ML. We then move to describe applications of ML methods in particle physics and cosmology, quantum many body physics, quantum computing, and chemical and material physics. We also highlight research and development into novel computing architectures aimed at accelerating ML. In each of the sections we describe recent successes as well as domain-specific methodology and challenges.