Phase Transition Study meets Machine Learning

Autor:	Ma, Yu-Gang, Pang, Long-Gang, Wang, Rui, Zhou, Kai
Rok vydání:	2023
Předmět:	Nuclear Theory High Energy Physics - Phenomenology
Zdroj:	Chinese Physics Letters 40, 122101 (2023)
Druh dokumentu:	Working Paper
DOI:	10.1088/0256-307X/40/12/122101
Popis:	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. Comment: arXiv admin note: text overlap with arXiv:2303.06752
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2311.07274 Zobrazit plný text záznamu View this record from Arxiv