Practical applications of machine-learned flows on gauge fields

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-...

Full description

Saved in:
Bibliographic Details
Published in:arXiv.org 2024-04
Main Authors: Abbott, Ryan, Albergo, Michael S, Boyda, Denis, Hackett, Daniel C, Kanwar, Gurtej, Romero-López, Fernando, Shanahan, Phiala E, Urban, Julian M
Format: Article
Language:English
Subjects:
Flow mapping
Quantum chromodynamics
Sampling
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2331-8422