Phase Transition Study meets Machine Learning

Abstract

In recent years, machine learning (ML) techniques have emerged as powerful tools in studying many-body complex systems, encompassing phase transitions in various domains of physics. This mini-review provides a concise yet comprehensive examination of the advancements achieved in applying ML for investigating phase transitions, with a primary emphasis on those involved in nuclear matter studies.