The effects of differential equation based knowledge embedding strategies in deep learning models: an investigation on how to model physico-chemical processes with neural networks
Gespeichert in:
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| Format: | Abschlussarbeit Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Berlin
Technische Universität Berlin
2024
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| Schlagworte: | |
| Online-Zugang: | Volltext Volltext Volltext https://d-nb.info/1349432318/34 |
| Beschreibung: | 1 Online-Ressource (xxvi, 187 Seiten) Illustrationen, Diagramme |
| DOI: | 10.14279/depositonce-22011 |
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| spelling | März, Christian-Thomas Verfasser aut The effects of differential equation based knowledge embedding strategies in deep learning models an investigation on how to model physico-chemical processes with neural networks vorgelegt von M. Sc. Christian-Thomas März Die Auswirkungen von auf Differentialgleichungen basierenden Wissenseinbettungsstrategien in Deep-Learning-Modellen Berlin Technische Universität Berlin 2024 1 Online-Ressource (xxvi, 187 Seiten) Illustrationen, Diagramme txt rdacontent c rdamedia cr rdacarrier Dissertation Technische Universität Berlin 2024 Archivierung/Langzeitarchivierung gewährleistet DE-101 pdager (DE-588)4113937-9 Hochschulschrift gnd-content Erscheint auch als Druck-Ausgabe (DE-604)BV050079374 https://doi.org/10.14279/depositonce-22011 Resolving-System kostenfrei Volltext https://hdl.handle.net/11303/23197 Resolving-System kostenfrei Volltext https://nbn-resolving.org/urn:nbn:de:101:1-2411270103313.752753900641 Resolving-System kostenfrei Volltext https://d-nb.info/1349432318/34 Langzeitarchivierung Nationalbibliothek 1\p emasg 0,37104 20241127 DE-101 https://d-nb.info/provenance/plan#emasg |
| spellingShingle | März, Christian-Thomas The effects of differential equation based knowledge embedding strategies in deep learning models an investigation on how to model physico-chemical processes with neural networks |
| subject_GND | (DE-588)4113937-9 |
| title | The effects of differential equation based knowledge embedding strategies in deep learning models an investigation on how to model physico-chemical processes with neural networks |
| title_alt | Die Auswirkungen von auf Differentialgleichungen basierenden Wissenseinbettungsstrategien in Deep-Learning-Modellen |
| title_auth | The effects of differential equation based knowledge embedding strategies in deep learning models an investigation on how to model physico-chemical processes with neural networks |
| title_exact_search | The effects of differential equation based knowledge embedding strategies in deep learning models an investigation on how to model physico-chemical processes with neural networks |
| title_full | The effects of differential equation based knowledge embedding strategies in deep learning models an investigation on how to model physico-chemical processes with neural networks vorgelegt von M. Sc. Christian-Thomas März |
| title_fullStr | The effects of differential equation based knowledge embedding strategies in deep learning models an investigation on how to model physico-chemical processes with neural networks vorgelegt von M. Sc. Christian-Thomas März |
| title_full_unstemmed | The effects of differential equation based knowledge embedding strategies in deep learning models an investigation on how to model physico-chemical processes with neural networks vorgelegt von M. Sc. Christian-Thomas März |
| title_short | The effects of differential equation based knowledge embedding strategies in deep learning models |
| title_sort | the effects of differential equation based knowledge embedding strategies in deep learning models an investigation on how to model physico chemical processes with neural networks |
| title_sub | an investigation on how to model physico-chemical processes with neural networks |
| topic_facet | Hochschulschrift |
| url | https://doi.org/10.14279/depositonce-22011 https://hdl.handle.net/11303/23197 https://nbn-resolving.org/urn:nbn:de:101:1-2411270103313.752753900641 https://d-nb.info/1349432318/34 |
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