发表机构
School of Nuclear Science and Technology, University of South China; INFN — Istituto Nazionale di Fisica Nucleare — Sezione di Bari; Key Laboratory of Quark and Lepton Physics (MOE) & Institute of Particle Physics, Central China Normal University; Southern Center for Nuclear-Science Theory (SCNT), Institute of Modern Physics, Chinese Academy of Sciences; School of Physics, East China Normal University; Key Laboratory of Nuclear Physics and Ion-beam Application (MOE), Institute of Modern Physics, Fudan University; Shanghai Research Center for Theoretical Nuclear Physics, NSFC and Fudan University; School of Science and Engineering, The Chinese University of Hong Kong (Shenzhen); School of Artificial Intelligence, The Chinese University of Hong Kong (Shenzhen)（南华大学核科学技术学院; 意大利国家核物理研究所巴里分部; 华中师范大学夸克与轻子物理教育部重点实验室及粒子物理研究所; 中国科学院近代物理研究所南方核科学理论中心; 华东师范大学物理系; 复旦大学核物理与离子束应用教育部重点实验室及近代物理研究所; 国家自然科学基金委与复旦大学上海理论核物理研究中心; 香港中文大学（深圳）理学院; 香港中文大学（深圳）人工智能学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本小型综述聚焦近年成熟的机器学习在高能核物理的应用，涵盖从模式识别到物理集成工作流的发展，强调ML融入物理工作流及物理约束的施加，为AI辅助物理结论提供支撑。
AI 中文摘要
高能核物理（HENP）中的机器学习（ML）正进入一个新阶段，物理知识被更直接地融入数据分析、模拟和物理推断中。本小型综述聚焦于过去几年已成熟的发展成果：早期应用侧重事例分类、模式识别及选定可观测量的代理模型，近期研究则转向物理集成工作流，包括QCD物质属性的校准贝叶斯提取、从重离子与中子星数据推断致密物质物态方程、生成式事例建模、弱物理信号的神经展开、可微逆求解器、规范等变与基于扩散的格点场采样器，以及全息QCD中模型函数的神经重建。我们综述了ML在重离子碰撞、中子星物理、格点QFT及全息或连续QCD中的近期应用，重点并非仅在ML架构，而是其如何融入具体物理工作流、如何施加对称性、守恒律、因果性、热力学稳定性及拓扑等物理约束，以及不确定性量化与验证如何判定AI辅助结果能否支持可靠的物理结论。
英文摘要
Machine learning (ML) in high-energy nuclear physics (HENP) is entering a new stage in which physical knowledge is incorporated more directly into data analysis, simulation, and physics inference. This mini-review focuses on developments that have matured in the past several years. Whereas earlier applications emphasized event classification, pattern recognition, and surrogate models for selected observables, recent work has moved toward physics-integrated workflows: calibrated Bayesian extraction of QCD matter properties, dense-matter equation-of-state inference from heavy-ion and neutron-star data, generative event modeling, neural unfolding of weak physical signals, differentiable inverse solvers, gauge-equivariant and diffusion-based lattice-field samplers, and neural reconstruction of model functions in holographic QCD. We survey recent applications of ML in heavy-ion collisions, neutron-star physics, lattice QFT, and holographic or continuum QCD. The emphasis is not on ML architectures alone, but on how they enter concrete physics workflows, how physical constraints such as symmetries, conservation laws, causality, thermodynamic stability, and topology are imposed, and how uncertainty quantification and validation determine whether an AI-assisted result can support a reliable physics conclusion.
Comments37 pages, 22 figures, NST accepted
