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.
@article{arxiv.2311.07274,
title = {Phase Transition Study meets Machine Learning},
author = {Yu-Gang Ma and Long-Gang Pang and Rui Wang and Kai Zhou},
journal= {arXiv preprint arXiv:2311.07274},
year = {2024}
}
Comments
arXiv admin note: text overlap with arXiv:2303.06752