TY  - CONF
AU  - Nicoli, Kim A.
AU  - Anders, Christopher J.
AU  - Funcke, Lena
AU  - Jansen, Karl
AU  - Nakajima, Shinichi
AU  - Kessel, Pan
TI  - NeuLat: a toolbox for neural samplers in lattice field theories
JO  - Proceedings of Science / International School for Advanced Studies
VL  - (LATTICE2023)
SN  - 1824-8039
CY  - Trieste
PB  - SISSA
M1  - PUBDB-2024-07803
T2  - 2752003
SP  - 286
PY  - 2024
AB  - 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
T2  - 40th International Symposium on Lattice Field Theory
CY  - 30 Jul 2023 - 5 Aug 2023, Batavia (United States)
Y2  - 30 Jul 2023 - 5 Aug 2023
M2  - Batavia, United States
KW  - lattice field theory (INSPIRE)
KW  - programming (INSPIRE)
KW  - flow (INSPIRE)
KW  - lattice (INSPIRE)
KW  - modular (INSPIRE)
KW  - machine learning (INSPIRE)
LB  - PUB:(DE-HGF)16 ; PUB:(DE-HGF)8 ; PUB:(DE-HGF)7
DO  - DOI:10.22323/1.453.0286
UR  - https://bib-pubdb1.desy.de/record/619664
ER  -