Title: Physics-Driven Learning for Inverse Problems in Quantum Chromodynamics Show Chinese title

Title: 基于物理的逆问题学习在量子色动力学中的应用

Abstract: The integration of deep learning techniques and physics-driven designs is reforming the way we address inverse problems, in which accurate physical properties are extracted from complex data sets. This is particularly relevant for quantum chromodynamics (QCD), the theory of strong interactions, with its inherent limitations in observational data and demanding computational approaches. This perspective highlights advances and potential of physics-driven learning methods, focusing on predictions of physical quantities towards QCD physics, and drawing connections to machine learning(ML). It is shown that the fusion of ML and physics can lead to more efficient and reliable problem-solving strategies. Key ideas of ML, methodology of embedding physics priors, and generative models as inverse modelling of physical probability distributions are introduced. Specific applications cover first-principle lattice calculations, and QCD physics of hadrons, neutron stars, and heavy-ion collisions. These examples provide a structured and concise overview of how incorporating prior knowledge such as symmetry, continuity and equations into deep learning designs can address diverse inverse problems across different physical sciences. Show Chinese abstract

Abstract: 将深度学习技术与物理驱动设计相结合正在改变我们解决逆问题的方式，其中从复杂数据集中提取准确的物理特性。 这对于量子色动力学（QCD）尤其重要，这是强相互作用的理论，其观测数据存在固有的限制，并且需要计算方法。 本文重点介绍了物理驱动学习方法的进展和潜力，专注于向QCD物理预测物理量，并将其与机器学习（ML）联系起来。 研究表明，机器学习与物理的融合可以带来更高效和可靠的解决问题策略。 介绍了机器学习的关键思想、嵌入物理先验的方法以及生成模型作为物理概率分布的反问题建模。 具体应用包括第一性原理格点计算，以及介子、中子星和重离子碰撞的QCD物理。 这些例子提供了一个结构化且简洁的概述，说明如何将对称性、连续性和方程等先验知识纳入深度学习设计，以解决不同物理科学中的各种逆问题。