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DTSTART;TZID=Europe/Stockholm:20230626T163000
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UID:submissions.pasc-conference.org_PASC23_sess163_msa141@linklings.com
SUMMARY:Generative Modeling and Smarter Sampling for Lattice Gauge Theorie
 s
DESCRIPTION:Minisymposium\n\nSam Foreman, James Osborn, and Xiao-Yong Jin 
 (Argonne National Laboratory)\n\nIn this work we describe how recent advan
 cements in generative modeling have contributed to simulations in lattice 
 gauge theory, and discuss some ongoing work in this direction. In particul
 ar, we are interested in generating independent (lattice) gauge configurat
 ions, distributed according to the density of our theory. In order to reli
 ably predict values which can be experimentally measured, simulations are 
 carried out at increasing spatial resolution and extrapolated to the conti
 nuum (infinite resolution) limit. In this limit, the cost of generating co
 nfigurations (using existing techniques) is known to scale exponentially, 
 quickly becoming prohibitively expensive, preventing further exploration. 
 In this work we present a new technique that uses a generalized version of
  the Hamiltonian Monte Carlo algorithm, parameterized by weights in a neur
 al network, that can be trained to make these simulations more efficient, 
 thereby decreasing their overall computational cost.\n\nDomain: Computer S
 cience, Machine Learning, and Applied Mathematics 
\n\nSession Chairs: Ric
 cardo Balin (Argonne National Laboratory) and Ramesh Balakrishnan (Argonne
  National Laboratory)
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