Journal Article/Contribution to a conference proceedings/Contribution to a book

PUBDB-2024-07803

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png NeuLat: a toolbox for neural samplers in lattice field theories

Nicoli, K. A. (Corresponding author) ; Anders, C. J. ; Funcke, L. ; Jansen, K.DESY* ; Nakajima, S. ; Kessel, P.

2024
SISSA Trieste

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Please use a persistent id in citations: doi:10.22323/1.453.0286  doi:10.3204/PUBDB-2024-07803

Abstract: The application of normalizing flows for sampling in lattice field theory has garnered considerable attention in recent years. Despite the growing community at the intersection of machine learning (ML) and lattice field theory, there is currently a lack of a software package that facilitates efficient software development for new ideas in this field. We present NeuLat, a fully customizable software package that unifies recent advances in the fast-growing field of deep generative models for lattice field theory in a single software library. NeuLat is designed to be modular, supports a variety of lattice field theories as well as normalizing flow architectures, and is easily extensible. We believe that NeuLat has the potential to considerably simplify the application and benchmarking of ML methods for lattice quantum field theories and beyond

Keyword(s): lattice field theory ; programming ; flow ; lattice ; modular ; machine learning

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Contributing Institute(s):

1. Centre f. Quantum Techno. a. Application (CQTA)

Research Program(s):

1. 611 - Fundamental Particles and Forces (POF4-611) (POF4-611)
2. QUEST - QUantum computing for Excellence in Science and Technology (101087126) (101087126)

Experiment(s):

1. No specific instrument

Appears in the scientific report 2024

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Database coverage:
; ; ; ; DOAJ Seal ; SCOPUS

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