Physics-Driven Learning for Inverse Problems in Quantum Chromodynamics
This addresses challenges in QCD physics, such as limited observational data and high computational demands, by providing a structured overview of methods, but it is incremental as it builds on existing integration of ML and physics.
The paper tackles inverse problems in quantum chromodynamics (QCD) by integrating physics-driven designs with deep learning, showing that this fusion leads to more efficient and reliable strategies for extracting physical properties from complex data.
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.