Speaker
Elia Cellini
(University of Edinburgh)
Description
In recent years, flow-based samplers have emerged as a promising alternative to traditional sampling methods in lattice gauge theory. In this talk, we will introduce a class of flow-based samplers known as Stochastic Normalizing Flows (SNFs), which combine neural networks with non-equilibrium Monte Carlo algorithms. We will show that SNFs exhibit excellent scaling with the volume in lattice SU$(3)$ gauge theory. Then, we will present an application to SU$(3)$ gauge theory with open boundary conditions, demonstrating how this approach represents an efficient strategy for the sampling of topological observables at fine lattice spacings.
| Parallel Session (for talks only) | Algorithms and artificial intelligence |
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Authors
Claudio Bonanno
(IFT UAM/CSIC Madrid)
Andrea Bulgarelli
(University of Turin and INFN Turin)
Elia Cellini
(University of Edinburgh)
Alessando Nada
(University of Turin)
Dario Panfalone
(University of Turin)
Davide Vadacchino
(University of Plymouth)
Lorenzo Verzichelli
(Università di Torino, INFN sezione di Torino)
