<div class="csl-bib-body">
<div class="csl-entry">Favoni, M., Ipp, A., Müller, D., & Schuh, D. (2023, March 2). <i>Generation of gauge field configurations with equivariant neural networks</i> [Conference Presentation]. Machine Learning approaches in Lattice QCD - An interdisciplinary exchange 2023, München, Germany.</div>
</div>
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/193316
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dc.description.abstract
In order to enhance the performance of neural networks in the context of lattice field theories, one can incorporate physical information directly into the network architecture. A key physical property of lattice gauge theories is gauge symmetry. Lattice gauge equivariant Convolutional Neural Networks (L-CNNs) are neural networks consisting of gauge equivariant layers that preserve this symmetry. Their application has focused on regression tasks on physical observables, i.e. fitting gauge field configurations to gauge invariant observables.
In this talk we want to address the possibility of applying L-CNNs to generating or modifying gauge field configurations. We make use of neural ordinary differential equations (neural ODEs) to modify arbitrary gauge configurations in a gauge equivariant way to produce new configurations. We exemplify this approach with practical tests on toy models based on Wilson (gradient) flow.
en
dc.language.iso
en
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dc.subject
Lattice Gauge Theory
en
dc.subject
Neural Networks
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dc.subject
Machine Learning
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dc.title
Generation of gauge field configurations with equivariant neural networks
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dc.type
Presentation
en
dc.type
Vortrag
de
dc.type.category
Conference Presentation
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tuw.publication.invited
invited
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tuw.researchTopic.id
C5
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tuw.researchTopic.name
Computer Science Foundations
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tuw.researchTopic.value
100
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tuw.publication.orgunit
E136 - Institut für Theoretische Physik
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tuw.author.orcid
0000-0001-9511-3523
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tuw.author.orcid
0000-0002-8163-7614
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tuw.author.orcid
0000-0001-7602-2503
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tuw.event.name
Machine Learning approaches in Lattice QCD - An interdisciplinary exchange 2023
en
tuw.event.startdate
27-02-2023
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tuw.event.enddate
03-03-2023
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tuw.event.online
On Site
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tuw.event.type
Event for scientific audience
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tuw.event.place
München
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tuw.event.country
DE
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tuw.event.institution
Institute for Advanced Study of the Technische Universität München
