Speaker
Javad Komijani
(ETH Zurich)
Description
In this talk, we present new neural network architectures inspired by effective field theories, designed to improve the scaling of the training cost for the generation of lattice field theory configurations using normalizing flows. Initially, we deal with poor acceptance rates in simulations of large lattices for scalar field theory in two dimensions and then discuss possible extensions to gauge theories in higher dimensions.
Primary author
Javad Komijani
(ETH Zurich)