Speaker
Description
Generative AI is rapidly becoming more than a tool for producing realistic images: in physics, it can be understood as a learnable machinery for transporting, sampling, and interrogating high-dimensional probability measures. In this talk I will discuss how this viewpoint opens new routes for exploring QCD matter under extreme conditions, where both first-principles theory and event-by-event simulations face severe computational bottlenecks. I will first introduce a unifying physical picture in which normalizing flows, diffusion models, flow matching, and stochastic path samplers are interpreted as finite-time nonequilibrium transports between probability distributions. I will then connect this framework to recent applications in lattice field theory, including diffusion models as stochastic quantization, the reconstruction of higher-order connected correlations, topology-aware sampling, and learning effective real distributions behind complex Langevin dynamics for complex actions. The second part of the talk will focus on heavy-ion collisions, where generative models can act as ultra-fast event-level surrogates of microscopic transport and hybrid simulations. In particular, I will highlight work developed in the joint BMBF-KISS program for HIC simulation sector, including point-cloud diffusion models such as HEIDi for generating complete UrQMD-like final-state hadron events rather than preselected observables. I will close by discussing how such generative models may interface with Bayesian inference, neural unfolding, and inverse problems, potentially enabling a new generation of simulation-accelerated, uncertainty-aware studies of the QCD phase structure.