Griese, Franziska und Knechtges, Philipp und Rüttgers, Alexander (2022) Hybrid Methods for Poisson and Stokes. WAW ML 8, 2022-11-07 - 2022-11-09, Jena, Germany.
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Kurzfassung
In this talk two different hybrid approaches which both combine physical knowledge with neural networks are examined. First, we consider physics-informed neural networks which embed the differential equations into the loss function of a neural network. Second, we present our novel hybrid approach which incorporates the residual of the finite element formulation on a discretization into the loss function of a neural network. Both methods are trained without data from simulations or measurements, but rely on the partial differential equation itself. Finally, we evaluate the methods applied to the Poisson equation and the Stokes flow.
elib-URL des Eintrags: | https://elib.dlr.de/191826/ | ||||||||||||||||
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Dokumentart: | Konferenzbeitrag (Vortrag) | ||||||||||||||||
Titel: | Hybrid Methods for Poisson and Stokes | ||||||||||||||||
Autoren: |
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Datum: | 7 November 2022 | ||||||||||||||||
Referierte Publikation: | Nein | ||||||||||||||||
Open Access: | Ja | ||||||||||||||||
Gold Open Access: | Nein | ||||||||||||||||
In SCOPUS: | Nein | ||||||||||||||||
In ISI Web of Science: | Nein | ||||||||||||||||
Status: | veröffentlicht | ||||||||||||||||
Stichwörter: | Finite Element Method, Machine Learning, Neural Netwok, Stokes problem, Hybrid Models, Physics Informed | ||||||||||||||||
Veranstaltungstitel: | WAW ML 8 | ||||||||||||||||
Veranstaltungsort: | Jena, Germany | ||||||||||||||||
Veranstaltungsart: | Workshop | ||||||||||||||||
Veranstaltungsbeginn: | 7 November 2022 | ||||||||||||||||
Veranstaltungsende: | 9 November 2022 | ||||||||||||||||
HGF - Forschungsbereich: | keine Zuordnung | ||||||||||||||||
HGF - Programm: | keine Zuordnung | ||||||||||||||||
HGF - Programmthema: | keine Zuordnung | ||||||||||||||||
DLR - Schwerpunkt: | Digitalisierung | ||||||||||||||||
DLR - Forschungsgebiet: | D KIZ - Künstliche Intelligenz | ||||||||||||||||
DLR - Teilgebiet (Projekt, Vorhaben): | D - PISA | ||||||||||||||||
Standort: | Köln-Porz | ||||||||||||||||
Institute & Einrichtungen: | Institut für Softwaretechnologie > High-Performance Computing Institut für Softwaretechnologie | ||||||||||||||||
Hinterlegt von: | Griese, Franziska | ||||||||||||||||
Hinterlegt am: | 20 Dez 2022 10:54 | ||||||||||||||||
Letzte Änderung: | 24 Apr 2024 20:53 |
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