Phase Transition Study meets Machine Learning
arXiv:2311.07274 · doi:10.1088/0256-307X/40/12/122101
Abstract
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.
arXiv admin note: text overlap with arXiv:2303.06752