Practical applications of machine-learned flows on gauge fields

arXiv:2404.11674v1 Announce Type: cross
Abstract: Normalizing flows are machine-learned maps between different lattice theories which can be used as components in exact sampling and inference schemes. Ongoing work yields increasingly expressive flows on gauge fields, but it remains an open question how flows can improve lattice QCD at state-of-the-art scales. We discuss and demonstrate two applications of flows in replica exchange (parallel tempering) sampling, aimed at improving topological mixing, which are viable with iterative improvements upon presently available flows.

abstract applications art arxiv components cond-mat.stat-mech cs.lg discuss fields hep-lat inference lattice machine maps practical question sampling state type work