This notebook tutorial demonstrates a method for sampling Boltzmann
distributions of lattice field theories using a class of machine learning
models known as normalizing flows. The ideas and approaches proposed in
arXiv:1904.12072, arXiv:2002.02428, and arXiv:2003.06413 are reviewed and a
concrete implementation of the framework is presented. We apply this framework
to a lattice scalar field theory and to U(1) gauge theory, explicitly encoding
gauge symmetries in the flow-based approach to the latter. This presentation is
intended to be interactive and working with the attached Jupyter notebook is
recommended.
Beschreibung
[2101.08176] Introduction to Normalizing Flows for Lattice Field Theory
%0 Journal Article
%1 albergo2021introduction
%A Albergo, Michael S.
%A Boyda, Denis
%A Hackett, Daniel C.
%A Kanwar, Gurtej
%A Cranmer, Kyle
%A Racanière, Sébastien
%A Rezende, Danilo Jimenez
%A Shanahan, Phiala E.
%D 2021
%K bayesian flows physics readings survey topology
%T Introduction to Normalizing Flows for Lattice Field Theory
%U http://arxiv.org/abs/2101.08176
%X This notebook tutorial demonstrates a method for sampling Boltzmann
distributions of lattice field theories using a class of machine learning
models known as normalizing flows. The ideas and approaches proposed in
arXiv:1904.12072, arXiv:2002.02428, and arXiv:2003.06413 are reviewed and a
concrete implementation of the framework is presented. We apply this framework
to a lattice scalar field theory and to U(1) gauge theory, explicitly encoding
gauge symmetries in the flow-based approach to the latter. This presentation is
intended to be interactive and working with the attached Jupyter notebook is
recommended.
@article{albergo2021introduction,
abstract = {This notebook tutorial demonstrates a method for sampling Boltzmann
distributions of lattice field theories using a class of machine learning
models known as normalizing flows. The ideas and approaches proposed in
arXiv:1904.12072, arXiv:2002.02428, and arXiv:2003.06413 are reviewed and a
concrete implementation of the framework is presented. We apply this framework
to a lattice scalar field theory and to U(1) gauge theory, explicitly encoding
gauge symmetries in the flow-based approach to the latter. This presentation is
intended to be interactive and working with the attached Jupyter notebook is
recommended.},
added-at = {2021-01-21T16:35:50.000+0100},
author = {Albergo, Michael S. and Boyda, Denis and Hackett, Daniel C. and Kanwar, Gurtej and Cranmer, Kyle and Racanière, Sébastien and Rezende, Danilo Jimenez and Shanahan, Phiala E.},
biburl = {https://www.bibsonomy.org/bibtex/25671acfef59794e906016d8b69595430/kirk86},
description = {[2101.08176] Introduction to Normalizing Flows for Lattice Field Theory},
interhash = {07031d8338c3f2e43460af83c0d6a8fa},
intrahash = {5671acfef59794e906016d8b69595430},
keywords = {bayesian flows physics readings survey topology},
note = {cite arxiv:2101.08176Comment: 38 pages, 5 numbered figures, Jupyter notebook included as ancillary file},
timestamp = {2021-01-21T16:35:50.000+0100},
title = {Introduction to Normalizing Flows for Lattice Field Theory},
url = {http://arxiv.org/abs/2101.08176},
year = 2021
}