About the appropriate neural network size for the engineering applications

dc.contributor.authorKovář, P.
dc.contributor.authorTater, A.
dc.contributor.authorPařez, J.
dc.contributor.authorFürst, J.
dc.contributor.editorAdámek, Vítězslav
dc.contributor.editorJonášová, Alena
dc.contributor.editorPlánička, Stanislav
dc.date.accessioned2023-12-06T16:32:19Z
dc.date.available2023-12-06T16:32:19Z
dc.date.issued2023
dc.description.sponsorshipThis work was supported by the grant agency of the Czech Technical University in Prague, grant No. SGS22/148/OHK2/3T/12. Authors also acknowledge support from the ESIF, EU Operational Programme Research, Devel opment and Education, and from the Center of Advanced Aerospace Technology (CZ.02.1.01/ 0.0/0.0/16 019/0000826), Faculty of Mechanical Engineering, Czech Technical University in Prague.en
dc.format4 s.cs
dc.format.mimetypeapplication/pdf
dc.identifier.citationComputational mechanics 2023: book of extended abstracts: 38th conference with international participation, p. 91-94.en
dc.identifier.isbn978-80-261-1177-1
dc.identifier.urihttp://hdl.handle.net/11025/54930
dc.language.isoenen
dc.publisherUniversity of West Bohemiaen
dc.rights© 2023 University of West Bohemia, Plzeň, Czech Republicen
dc.rights.accessopenAccessen
dc.subjectnervová síťcs
dc.subjectinženýrstvícs
dc.subjecthlucs
dc.subject.translatedneural networken
dc.subject.translatedengineeringen
dc.subject.translateddeep learningen
dc.titleAbout the appropriate neural network size for the engineering applicationsen
dc.typekonferenční příspěvekcs
dc.typeconferenceObjecten
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen

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