Physics-Driven Learning for Inverse Problems in Quantum Chromodynamics

TL;DR

This paper discusses how integrating physics principles with machine learning enhances the solution of inverse problems in quantum chromodynamics, improving efficiency and reliability in predicting physical properties from complex data.

Contribution

It introduces a framework for embedding physics priors into deep learning models to better solve inverse problems in QCD and related physical sciences.

Findings

Physics-driven ML improves accuracy of QCD property predictions

Embedding physical priors enhances model reliability

Applications include lattice QCD, hadron physics, neutron stars, and heavy-ion collisions

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.