<div class="csl-bib-body">
<div class="csl-entry">Holland, K., Ipp, A., Müller, D. I., & Wenger, U. (2024, July 26). <i>Lattice simulations with machine-learned classically perfect fixed-point actions</i> [Conference Presentation]. ML meets LFT 2024, Swansea University, United Kingdom of Great Britain and Northern Ireland (the). http://hdl.handle.net/20.500.12708/210663</div>
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/210663
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dc.description.abstract
Fixed-point actions are classically perfect lattice actions, i.e., they are free from classical lattice artifacts. Furthermore, they exhibit suppressed quantum artifacts. Monte Carlo simulations employing such actions may provide a way to efficiently approach the continuum limit on coarse lattices, thereby avoiding critical slowing down and topological freezing. Extending our previous work, we use lattice gauge equivariant convolutional neural networks (L-CNNs) to approximate a fixed-point action for SU(3) gauge theory in four dimensions to previously unseen accuracy. Using this new parametrization, we perform HMC simulations and classically perfect gradient flow. Our self-consistent approach aims to extract gradient flow observables on much coarser lattices compared to simulations using the Wilson action.
en
dc.description.sponsorship
FWF - Österr. Wissenschaftsfonds
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dc.language.iso
en
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dc.subject
Machine Learning
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dc.subject
Lattice Gauge Theory
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dc.subject
Monte Carlo Simulation
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dc.title
Lattice simulations with machine-learned classically perfect fixed-point actions
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dc.type
Presentation
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dc.type
Vortrag
de
dc.contributor.affiliation
University of Bern, Switzerland
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dc.relation.grantno
P 34764-N
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dc.type.category
Conference Presentation
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tuw.publication.invited
invited
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tuw.project.title
Simulation der frühesten Stadien von Schwerionenkollisionen
