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dc.contributor.author
Kumar, Siddhant
dc.contributor.author
Kochmann, Dennis M.
dc.contributor.editor
Aldakheel, Fadi
dc.contributor.editor
Hudobivnik, Blaž
dc.contributor.editor
Soleimani, Meisam
dc.contributor.editor
Wessels, Henning
dc.contributor.editor
Weißenfels, Christian
dc.contributor.editor
Marino, Michele
dc.date.accessioned
2023-05-04T08:46:02Z
dc.date.available
2023-05-04T03:33:00Z
dc.date.available
2023-05-04T08:46:02Z
dc.date.issued
2022-03
dc.identifier.isbn
978-3-030-87311-0
en_US
dc.identifier.isbn
978-3-030-87312-7
en_US
dc.identifier.other
10.1007/978-3-030-87312-7_27
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/610514
dc.description.abstract
Machine learning has found its way into almost every area of science and engineering, and we are only at the beginning of its exploration across fields. Being a popular, versatile and powerful framework, machine learning has provenmost useful where classical techniques are computationally inefficient, which applies particularly to computational solid mechanics. Here, we dare to give a non-exhaustive overview of potential avenues for machine learning in the numerical modeling of solids and structures and offer our (subjective) perspective on what is yet to come.
en_US
dc.language.iso
en
en_US
dc.publisher
Springer
en_US
dc.title
What Machine Learning Can Do for Computational Solid Mechanics
en_US
dc.type
Book Chapter
dc.date.published
2022-03-13
ethz.book.title
Current Trends and Open Problems in Computational Mechanics
en_US
ethz.pages.start
275
en_US
ethz.pages.end
285
en_US
ethz.identifier.scopus
ethz.publication.place
Cham
en_US
ethz.publication.status
published
en_US
ethz.date.deposited
2023-05-04T03:33:00Z
ethz.source
SCOPUS
ethz.eth
yes
en_US
ethz.availability
Metadata only
en_US
ethz.rosetta.installDate
2024-02-02T22:34:47Z
ethz.rosetta.lastUpdated
2024-02-02T22:34:47Z
ethz.rosetta.versionExported
true
ethz.COinS
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