Up-Net: a generic deep learning-based time stepper for parameterized spatio-temporal dynamics

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Location: Deutsche Nationalbibliothek Frankfurt am Main

Extent: Online-Ressource

Language: Englisch

Bibliographic citation: In: Stender, M.; Ohlsen, J.; Geisler, H.; Chabchoub, A.; Hoffmann, N. et al.: Up-Net: a generic deep learning-based time stepper for parameterized spatio-temporal dynamics. In: Computational Mechanics 71 (2023), Nr. 6, S. 1227-1249. DOI: https://doi.org/10.1007/s00466-023-02295-x

Event: Veröffentlichung

(where): Hannover

(who): Gottfried Wilhelm Leibniz Universität Hannover

(when): 2023

Event: Veröffentlichung

(where): Hannover

(who): Technische Informationsbibliothek (TIB)

(when): 2023

Creator: Stender, Merten
Ohlsen, Jakob
Geisler, Hendrik
Chabchoub, Amin
Hoffmann, Norbert
Schlaefer, Alexander

DOI: 10.15488/14757

URN: urn:nbn:de:101:1-2023092802223065219235

Rights: Der Zugriff auf das Objekt ist unbeschränkt möglich.

Last update: 14.08.2025, 11:01 AM CEST

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Associated

Stender, Merten
Ohlsen, Jakob
Geisler, Hendrik
Chabchoub, Amin
Hoffmann, Norbert
Schlaefer, Alexander
Gottfried Wilhelm Leibniz Universität Hannover
Technische Informationsbibliothek (TIB)

Time of origin

2023

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Up-Net: a generic deep learning-based time stepper for parameterized spatio-temporal dynamics

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Other Objects (12)

Up-Net : a generic deep learning-based time stepper for parameterized spatio-temporal dynamics

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