Shared Data and Algorithms for Deep Learning in Fundamental Physics

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Location: Deutsche Nationalbibliothek Frankfurt am Main

Extent: Online-Ressource

Language: Englisch

Bibliographic citation: In: 10.1007/s41781-022-00082-6

Event: Veröffentlichung

(where): Aachen

(who): Universitätsbibliothek der RWTH Aachen

(when): 2022

Creator: Benato, Lisa
Buhmann, Erik
Erdmann, Martin
Fackeldey, Peter
Glombitza, Jonas
Hartmann, Nikolai
Kasieczka, Gregor
Korcari, William
Kuhr, Thomas
Steinheimer, Jan
Stöcker, Horst
Plehn, Tilman
Zhou, Kai

DOI: 10.18154/RWTH-2022-05195

URN: urn:nbn:de:101:1-2022052703251437195777

Rights: Der Zugriff auf das Objekt ist unbeschränkt möglich.

Last update: 25.03.2025, 1:54 PM CET

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Associated

Benato, Lisa
Buhmann, Erik
Erdmann, Martin
Fackeldey, Peter
Glombitza, Jonas
Hartmann, Nikolai
Kasieczka, Gregor
Korcari, William
Kuhr, Thomas
Steinheimer, Jan
Stöcker, Horst
Plehn, Tilman
Zhou, Kai
Universitätsbibliothek der RWTH Aachen

Time of origin

2022

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Shared Data and Algorithms for Deep Learning in Fundamental Physics

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Other Objects (12)

Deep Learning based Algorithms in Astroparticle Physics

Strings and fundamental physics

Optimization algorithms in physics

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Nuclear clocks for testing fundamental physics

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