Speaker
Description
Recent advances in machine learning have unlocked transformative approaches to longstanding challenges in fundamental physics. In this talk, I will present our latest work that harnesses physics‐driven deep learning to tackle two intertwined frontiers: solving inverse problems in Quantum Chromodynamics (QCD) and deploying generative models for statistical physics and field theory.
Inverse problems in QCD—such as the reconstruction of hadronic spectral functions and the extraction of the dense matter equation of state—are inherently ill‐posed, with traditional methods often falling short of reliably capturing subtle physical details. By embedding physical priors and symmetry constraints directly into neural network architectures and leveraging automatic differentiation, our approach significantly improves the precision and stability of the extracted observables.
In parallel, we have explored the use of advanced generative models—including diffusion models and Fourier‐flow techniques—as global samplers in lattice field theory. These methods recast stochastic quantization into a machine‐learned framework, mitigating challenges like critical slowing down and topological freezing while enabling efficient sampling of complex quantum configurations.
Together, these innovations illustrate how machine learning can bridge data‐driven insights with rigorous physics principles to decode the rich phenomenology of QCD matter under extreme conditions. This presentation will outline our methodologies, highlight key numerical and theoretical results, and discuss the promising prospects for AI-driven research in fundamental physics.
| AI keywords | simulation-based inference, generative models, automatic differentiation, diffusion models, ML based inverse problems |
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