<div class="csl-bib-body">
<div class="csl-entry">Aronsson, J., Müller, D., & Schuh, D. (2023). <i>Geometrical aspects of lattice gauge equivariant convolutional neural networks</i>. arXiv. https://doi.org/10.48550/arXiv.2303.11448</div>
</div>
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/191965
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dc.description.abstract
Lattice gauge equivariant convolutional neural networks (L-CNNs) are a framework for convolutional neural networks that can be applied to non-Abelian lattice gauge theories without violating gauge symmetry. We demonstrate how L-CNNs can be equipped with global group equivariance. This allows us to extend the formulation to be equivariant not just under translations but under global lattice symmetries such as rotations and reflections. Additionally, we provide a geometric formulation of L-CNNs and show how convolutions in L-CNNs arise as a special case of gauge equivariant neural networks on SU(N) principal bundles.
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
Convolutional Neural Networks
en
dc.subject
Lattice Gauge Theory
en
dc.title
Geometrical aspects of lattice gauge equivariant convolutional neural networks
en
dc.type
Preprint
en
dc.type
Preprint
de
dc.identifier.arxiv
2303.11448
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dc.contributor.affiliation
Chalmers University of Technology, Sweden
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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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tuw.project.title
Glasma-Simulationen mit maschinellem Lernen hochskalieren
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tuw.project.title
Simulation der frühesten Stadien von Schwerionenkollisionen