相变研究与机器学习

IF 3.5 2区物理与天体物理 Q1 PHYSICS, MULTIDISCIPLINARY

Chinese Physics Letters Pub Date : 2023-11-09 DOI:10.1088/0256-307x/40/12/122101

Yu-Gang Ma, Long-Gang Pang, Rui Wang, Kai Zhou

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引用次数: 1

摘要

近年来，机器学习(ML)技术已成为研究多体复杂系统的强大工具，涵盖了物理各个领域的相变。这篇迷你评论提供了一个简明而全面的检查，在应用ML研究相变方面取得的进展，主要强调那些涉及核物质研究的进展。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Phase Transition Study meets Machine Learning

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.

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来源期刊

Chinese Physics Letters 物理-物理：综合

CiteScore

5.90

自引率

8.60%

发文量

13238

审稿时长

4 months

期刊介绍： Chinese Physics Letters provides rapid publication of short reports and important research in all fields of physics and is published by the Chinese Physical Society and hosted online by IOP Publishing.