NeuLat: a toolbox for neural samplers in lattice field theories - INSPIRE

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NeuLat: a toolbox for neural samplers in lattice field theories

Kim A. Nicoli
(
- Tata Inst. and
- Bonn U., HISKP and
- Bonn U. and
- Berlin, Tech. U.
)
,
Christopher J. Anders
(
- Berlin, Tech. U.
)
,
Lena Funcke
(
- Tata Inst. and
- Bonn U., HISKP and
- Bonn U.
)
,
Karl Jansen
(
- DESY, Zeuthen
)
,
Shinichi Nakajima
(
- Berlin, Tech. U. and
- IBM, Tokyo
)

Dec 27, 2023

13 pages

Published in:

PoS LATTICE2023 (2024) 286

Contribution to:

- Lattice 2023
, 286

Published: Dec 27, 2023

DOI:

10.22323/1.453.0286

reference search5 citations

Citations per year

Abstract: (SISSA)

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

lattice field theory
programming
flow
lattice
modular
machine learning

References(56)

Figures(0)

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)
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)
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)
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(
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)
,
Alessandro Nada
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(
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,
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(
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)
,
Miranda C.N. Cheng
(
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(
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)
,
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)
,
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Gurtej Kanwar
(
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)
,
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(
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)
,
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(
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)
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(
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(
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)
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(
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(
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(
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(
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(
- New York U., CCPP
)
,
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(
- MIT, Cambridge, CTP and
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)
,
Sébastien Racanière
(
- Unlisted, UK
)
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Ryan Abbott
(
- MIT, Cambridge, CTP and
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)
,
Michael S. Albergo
(
- New York U., CCPP
)
,
Denis Boyda
(
- Argonne, ALCF and
- MIT, Cambridge, CTP and
- IAIFI, Cambridge
)
,
Kyle Cranmer
(
- New York U., CCPP
)
,
Daniel C. Hackett
(
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)

et al.

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Michele Caselle
(
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)
,
Elia Cellini
(
- Turin U. and
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)
,
Alessandro Nada
(
- Turin U. and
- INFN, Turin
)

- JHEP 02 (2024) 048
•
e-Print:
- 2307.01107
•
DOI:
- 10.1007/JHEP02(2024)048

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