In recent years, machine learning has emerged as a powerful computational tool and novel problem-solving perspective for physics, offering new avenues for studying strongly interacting QCD matter properties under extreme conditions. This review article aims to provide an overview of the current state of this intersection of fields, focusing on the application of machine learning to theoretical studies in high energy nuclear physics. It covers diverse aspects, including heavy ion collisions, lattice field theory, and neutron stars, and discuss how machine learning can be used to explore and facilitate the physics goals of understanding QCD matter. The review also provides a commonality overview from a methodology perspective, from data-driven perspective to physics-driven perspective. We conclude by discussing the challenges and future prospects of machine learning applications in high energy nuclear physics, also underscoring the importance of incorporating physics priors into the purely data-driven learning toolbox. This review highlights the critical role of machine learning as a valuable computational paradigm for advancing physics exploration in high energy nuclear physics.

近年来，机器学习已成为一种强大的计算工具和新颖的物理学问题解决视角，为研究极端条件下强相互作用的 QCD 物质特性提供了新的途径。本文旨在概述这一交叉领域的现状，重点关注机器学习在高能核物理理论研究中的应用。它涵盖了不同的方面，包括重离子碰撞、晶格场理论和中子星，并讨论了如何使用机器学习来探索和促进理解 QCD 物质的物理目标。该评论还从方法论角度、从数据驱动的角度到物理驱动的角度提供了共性概述。最后，我们讨论了机器学习在高能核物理中应用的挑战和未来前景，还强调了将物理先验纳入纯粹数据驱动的学习工具箱的重要性。这篇综述强调了机器学习作为一种有价值的计算范式对于推进高能核物理领域物理探索的关键作用。