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<div class="csl-entry">Ipp, A., Holland, K., Müller, D. I., & Wenger, U. (2024, July 23). <i>Symmetries and Generalization for Machine Learning on a Lattice</i> [Presentation]. RIKEN iTHEMS DEEP-IN Seminar 2024, RIKEN Wako, Tokio, Japan. http://hdl.handle.net/20.500.12708/208966</div>
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Symmetries such as translations and rotations are crucial in physics and machine learning. The global symmetry of translations leads to convolutional neural networks (CNNs), while the much larger space of local gauge symmetry has driven us to develop lattice gauge equivariant convolutional neural networks (L-CNNs). This talk will discuss how the challenges of simulating the earliest stage of heavy ion collisions led us to use machine learning and how these innovations could improve lattice simulations in the future.
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
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dc.subject
Lattice Gauge Theory
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dc.title
Symmetries and Generalization for Machine Learning on a Lattice
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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
Presentation
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tuw.publication.invited
invited
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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
