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Phase Transition Study meets Machine Learning
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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.
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Cited by 2 Pith papers
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Heavy Quarkonium Spectrum and Decay Constants from a Neural-Network-Based Holographic Model
A neural-network-parametrized dilaton field reproduces the masses and leptonic decay constants of charmonium and bottomonium with 1.26% and 3.32% RMS errors, but only because those values were used as training data.
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Machine Learning Insights into Quark-Antiquark Interactions: Probing Field Distributions and String Tension in QCD
A machine-learning fit to lattice chromo field data yields a compact two-variable expression for E(d, xt) and reproduces flux tube string tension and width over existing separations.
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