Record link: 
http://hdl.handle.net/20.500.12708/208965
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Title: 
Machine learning fixed point actions with lattice gauge equivariant convolutional neural networks
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Citation: 
Ipp, A., Holland, K., Müller, D. I., & Wenger, U. (2024, July 16). Machine learning fixed point actions with lattice gauge equivariant convolutional neural networks [Presentation]. HAL QCD Collaboration Meeting 2024, RIKEN Wako, Tokio, Japan. http://hdl.handle.net/20.500.12708/208965
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Publication Type: 
Presentation - Presentation
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Language: 
English
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Authors: 
Ipp, Andreas 
Holland, Kieran 
Müller, David I. 
Wenger, Urs 
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Organisational Unit: 
E136 - Institut für Theoretische Physik 
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Date (published): 
16-Jul-2024
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Event name: 
HAL QCD Collaboration Meeting 2024 
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Event date: 
16-Jul-2024
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Event place: 
RIKEN Wako, Tokio, Japan
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Keywords: 
Machine Learning; Lattice Gauge Theory
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Research facilities: 
Vienna Scientific Cluster 
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Project title: 
Glasma-Simulationen mit maschinellem Lernen hochskalieren: P 32446-N27 (FWF - Österr. Wissenschaftsfonds) 
Simulation der frühesten Stadien von Schwerionenkollisionen: P 34764-N (FWF - Österr. Wissenschaftsfonds) 
Nichtperturbative Eigenschaften evolvierenden gluonischer Plasmen: P 34455-N (FWF - Österr. Wissenschaftsfonds) 
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Research Areas: 
Quantum Modeling and Simulation: 40%
Modeling and Simulation: 60%
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Science Branch: 
1020 - Informatik: 40%
1030 - Physik, Astronomie: 60%
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