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DNN-MG: A hybrid neural network/finite element method with applications to 3D simulations of the Navier–Stokes equations.

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Title: DNN-MG: A hybrid neural network/finite element method with applications to 3D simulations of the Navier–Stokes equations.
Authors: Margenberg, Nils1, margenbn@hsu-hh.de, Jendersie, Robert2,3, robert.jendersie@ovgu.de, Lessig, Christian3, christian.lessig@ovgu.de, Richter, Thomas2, thomas.richter@ovgu.de
Source: Computer Methods in Applied Mechanics & Engineering; Feb2024, Vol. 420, pN.PAG-N.PAG, 1p
Database: Applied Science & Technology Source
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  Availability: 0
Header DbId: aci
DbLabel: Applied Science & Technology Source
An: 175026707
AccessLevel: 2
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Data: <searchLink fieldCode="JN" term="%22Computer+Methods+in+Applied+Mechanics+%26+Engineering%22">Computer Methods in Applied Mechanics & Engineering</searchLink>; Feb2024, Vol. 420, pN.PAG-N.PAG, 1p
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=aci&AN=175026707
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1016/j.cma.2023.116692
    Languages:
      – Code: eng
        Text: English
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      Pagination:
        PageCount: 1
        StartPage: N.PAG
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      – TitleFull: DNN-MG: A hybrid neural network/finite element method with applications to 3D simulations of the Navier–Stokes equations.
        Type: main
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            NameFull: Margenberg, Nils
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            NameFull: Jendersie, Robert
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            NameFull: Lessig, Christian
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            – D: 15
              M: 02
              Text: Feb2024
              Type: published
              Y: 2024
          Identifiers:
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              Value: 00457825
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            – Type: volume
              Value: 420
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            – TitleFull: Computer Methods in Applied Mechanics & Engineering
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