Phase Transition Study Meets Machine Learning

doi:10.1088/0256-307X/40/12/122101

Phase Transition Study Meets Machine Learning

2023-12-01

49

OA

AI

DOI：10.1088/0256-307X/40/12/122101

Original

Original request for help

Save

Abstract

En 中文

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

Keywords:

QUANTUM CHROMODYNAMICS

NUCLEAR

Phase Transition Study Meets Machine Learning

Abstract

Journal

Organization