Machine learning for four-dimensional SU(3) lattice gauge theories
In this review I summarize how machine learning can be used in lattice gauge theory simulations and what ap\-proaches are currently available to improve the sampling of gauge field configurations, with a focus on applications in four-dimensional SU(3) gauge theories. These include approaches based on generative machine-learning models such as (stochastic) normalizing flows and diffusion processes, and an approach based on renormalization group (RG) transformations, more specifically the machine learning of RG-improved gauge actions using gauge-equivariant convolutional neural networks. In particular, I present scaling results for a machine-learned fixed-point action in four-dimensional SU...
| Authors | Urs Wenger |
|---|---|
| Primary category | hep-lat |
| Published | 14 April 2026 |
| Last updated at source | 14 April 2026 |
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