/
_quarto.yml
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/
_quarto.yml
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project:
type: website
output-dir: docs
preview:
port: 4204
title: "MLMC: Machine Learning Monte Carlo"
date: today
date-modified: last-modified
author-title: ""
affiliation-title: ""
published-title: ""
modified-title: ""
title-block-categories: false
number-sections: false
bibliography: references.bib
appendix-cite-as: display
favicon: "./assets/favicon.svg"
twitter-card:
image: "https://github.com/saforem2/lattice23/blob/main/assets/thumbnail.png?raw=true"
site: "@saforem2"
creator: "@saforem2"
author:
name: Sam Foreman
url: https://samforeman.me
orcid: 0000-0002-9981-0876
email: foremans@anl.gov
affiliation: Argonne National Laboratory
affiliation-url: https://alcf.anl.gov/about/people/sam-foreman
citation:
author: Sam Foreman
type: speech
genre: "Presentation at the 2023 International Symposium on Lattice Field Theory"
container-title: https://indico.fnal.gov/event/57249/contributions/271305/
title: "MLMC: Machine Learning Monte Carlo for Lattice Gauge Theory"
url: https://saforem2.github.io/lattice23
abstract: |
We present a trainable framework for efficiently generating gauge
configurations, and discuss ongoing work in this direction. In particular, we
consider the problem of sampling configurations from a 4D 𝑆𝑈(3) lattice gauge
theory, and consider a generalized leapfrog integrator in the molecular
dynamics update that can be trained to improve sampling efficiency.
# -----------------------
# type: article-journal
# container-title: ACM Transactions on Embedded Computing Systems
# volume: 21
# issue: 2
# issued: 2022-03
# issn: 1539-9087
# doi: 10.1145/3514174
# -----------------------
# type: speech
# container-title: "Journal of Data Science Software"
# doi: "10.23915/reprodocs.00010"
# url: https://example.com/summarizing-output
# -----------------------
google-scholar: true
google-analytics: G-XVM2Y822Y1
#
#
#
#
#
# # website:
# # page-navigation: true
# # title: "MLMC: Machine Learning Monte Carlo"
# # # site-url: "https://saforem2.github.io/mlmc"
# # # favicon: "./assets/favicon.svg"
# appendix-cite-as: display
# citation:
# type: speech
# genre: "Presentation at the 2023 International Symposium on Lattice Field Theory"
# container-title: https://indico.fnal.gov/event/57249/contributions/271305/
# title: "MLMC: Machine Learning Monte Carlo for Lattice Gauge Theory"
# url: https://saforem2.github.io/lattice23
# author:
# - name: Sam Foreman
# # id: sf
# url: https://samforeman.me
# orcid: 0000-0002-9981-0876
# email: foremans@anl.gov
# affiliation: "Argonne National Laboratory"
# affiliation-url: https://alcf.anl.gov/about/people/sam-foreman
#
# # affiliation:
# # - name: Argonne National Laboratory
# # city: Chicago
# # state: IL
# # url: https://alcf.anl.gov
# #
# favicon: "./assets/favicon.svg"
# google-analytics: G-XVM2Y822Y1
# # title: A Published Conference Paper
# # author:
# # - name: Norah Jones
# # affiliation: School of Hard Knocks
# # orcid: 0000-0001-8715-9476
# # citation:
# # type: paper-conference
# # container-title: "Proceedings of the annual conference of the Society for Research"
# # publisher: "Society for Research"
# # issued: 2020/09/23
# # volume: 2
# # doi: "10.23915/reprodocs.00010"
# # url: https://example.com/summarizing-output
# # page-first: 46
# # page-last: 53
# # editor:
# # - Don Draper
# # - Nick Fury
# # google-scholar: true
# #
# #
# # affiliation: ""
# # date: ""
# # toc: true
# filters:
# - lua/callouts.lua
# highlight-style:
# dark: dracula
# light: dracula-light