DC Element
Wert
Sprache
dc.contributor.author
Bohn, Jan
-
dc.contributor.author
Feischl, Michael
-
dc.date.accessioned
2022-12-23T15:56:19Z
-
dc.date.available
2022-12-23T15:56:19Z
-
dc.date.issued
2021
-
dc.identifier.citation
<div class="csl-bib-body"> <div class="csl-entry">Bohn, J., &#38; Feischl, M. (2021). Recurrent neural networks as optimal mesh refinement strategies. <i>Computers and Mathematics with Applications</i>, <i>97</i>, 61–76. https://doi.org/10.1016/j.camwa.2021.05.018</div> </div>
-
dc.identifier.issn
0898-1221
-
dc.identifier.uri
http://hdl.handle.net/20.500.12708/138383
-
dc.language.iso
en
-
dc.relation.ispartof
Computers and Mathematics with Applications
-
dc.subject
Modeling and Simulation
-
dc.subject
Computational Mathematics
-
dc.subject
Computational Theory and Mathematics
-
dc.title
Recurrent neural networks as optimal mesh refinement strategies
en
dc.type
Artikel
de
dc.type
Article
en
dc.description.startpage
61
-
dc.description.endpage
76
-
dc.type.category
Original Research Article
-
tuw.container.volume
97
-
tuw.journal.peerreviewed
true
-
tuw.peerreviewed
true
-
tuw.researchTopic.id
C4
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tuw.researchTopic.name
Mathematical and Algorithmic Foundations
-
tuw.researchTopic.value
100
-
dcterms.isPartOf.title
Computers and Mathematics with Applications
-
tuw.publication.orgunit
E101-02 - Forschungsbereich Numerik
-
tuw.publisher.doi
10.1016/j.camwa.2021.05.018
-
dc.identifier.eissn
1873-7668
-
dc.description.numberOfPages
16
-
wb.sci
true
-
wb.sciencebranch
Mathematik
-
wb.sciencebranch.oefos
1010
-
wb.facultyfocus
Analysis und Scientific Computing
de
wb.facultyfocus
Analysis and Scientific Computing
en
wb.facultyfocus.faculty
E100
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item.languageiso639-1
en
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item.openairetype
research article
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item.grantfulltext
none
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item.fulltext
no Fulltext
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item.cerifentitytype
Publications
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item.openairecristype
http://purl.org/coar/resource_type/c_2df8fbb1
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crisitem.author.dept
E101-02-3 - Forschungsgruppe Computational PDEs
-
crisitem.author.parentorg
E101-02 - Forschungsbereich Numerik
-
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