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Phase Transition Study Meets Machine Learning
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DOI：10.1088/0256-307X/40/12/122101.png)
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Abstract
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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
Journal
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4.2
Papers:
9.1K
Citations:
7.7K
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