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Data- and physics-driven deep learning for forward and inverse problems in computational mechanics
dc.contributor.author
Bastek, Jan-Hendrik
dc.date.accessioned
2024-11-22T12:44:29Z
dc.date.available
2024-11-22T12:44:29Z
dc.date.issued
2024
dc.identifier.uri
http://hdl.handle.net/20.500.11850/706657
dc.title
Data- and physics-driven deep learning for forward and inverse problems in computational mechanics
en_US
dc.type
Doctoral Thesis
ethz.code.ddc
DDC - DDC::6 - Technology, medicine and applied sciences::620 - Engineering & allied operations
en_US
ethz.identifier.diss
30773
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02130 - Dep. Maschinenbau und Verfahrenstechnik / Dep. of Mechanical and Process Eng.::02618 - Institut für Mechanische Systeme / Institute of Mechanical Systems::09600 - Kochmann, Dennis / Kochmann, Dennis
en_US
ethz.date.deposited
2024-11-22T12:44:30Z
ethz.source
FORM
ethz.eth
yes
en_US
ethz.doipreview
Yes
en_US
ethz.rosetta.exportRequired
true
ethz.COinS
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Publication type
-
Doctoral Thesis [30333]