Speaker
Dr
Christopher Anders
Description
Normalizing flows have increasingly gained attention as a promising
choice for sampling in lattice field theory. However, there has been a
lack of software packages for applying powerful generative Machine
Learning models such as Normalizing flows specifically for lattice field
theory. To fill this gap, we present NeuLat: a fully customizable
software package that allows researchers in lattice field theory to
harness the recent advances in deep generative learning. In this
hands-on session, we explore how NeuLat can considerably simplify the
application and benchmarking of deep generative models for lattice
quantum field theory.
