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<div class="csl-entry">Wenger, U., Holland, K., & Ipp, A. (2024, July 30). <i>HMC and gradient flow with machine-learned classically perfect fixed point actions</i> [Poster Presentation]. Lattice 2024, Liverpool, United Kingdom of Great Britain and Northern Ireland (the). http://hdl.handle.net/20.500.12708/208987</div>
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Fixed point (FP) lattice actions are classically perfect, i.e., they have continuum classical properties unaffected by discretization effects. They have suppressed lattice artefacts and therefore provide a possible way to extract continuum physics with coarser lattices, allowing to circumvent problems with critical slowing down and topological freezing towards the continuum limit. We use machine-learning methods to parameterize a FP action for four-dimensional SU(3) gauge theory using lattice gauge-covariant convolutional neural networks (L-CNNs). The large operator space allows us to find superior parametrizations compared to previous studies and we show how such actions can be efficiently simulated with HMC algorithms. Furthermore, we argue that FP lattice actions can be used to define a classically perfect gradient flow without any lattice artefacts at tree level.
en
dc.description.sponsorship
FWF - Österr. Wissenschaftsfonds
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dc.description.sponsorship
FWF - Österr. Wissenschaftsfonds
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dc.description.sponsorship
FWF - Österr. Wissenschaftsfonds
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dc.language.iso
en
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dc.subject
Machine Learning
en
dc.subject
Lattice Gauge Theory
en
dc.title
HMC and gradient flow with machine-learned classically perfect fixed point actions
en
dc.type
Presentation
en
dc.type
Vortrag
de
dc.contributor.affiliation
University of Bern, Switzerland
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dc.relation.grantno
P 32446-N27
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dc.relation.grantno
P 34764-N
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dc.relation.grantno
P 34455-N
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dc.type.category
Poster Presentation
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tuw.project.title
Glasma-Simulationen mit maschinellem Lernen hochskalieren
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tuw.project.title
Simulation der frühesten Stadien von Schwerionenkollisionen
