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  Application of artificial neural networks for the prediction of interface mechanics: a study on grain boundary constitutive behavior

Fernández, M., Rezaei, S., Mianroodi, J. R., Fritzen, F., & Reese, S. (2020). Application of artificial neural networks for the prediction of interface mechanics: a study on grain boundary constitutive behavior. Advanced Modeling and Simulation in Engineering Sciences, 7: 1. doi:10.1186/s40323-019-0138-7.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0006-B16A-B Version Permalink: http://hdl.handle.net/21.11116/0000-0006-B16B-A
Genre: Journal Article

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amses-journal.springeropen.comtrackpdf10.1186s40323-019-0138-7.pdf (Supplementary material), 5MB
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 Creators:
Fernández, Mauricio1, 2, Author
Rezaei, Shahed3, Author              
Mianroodi, Jaber Rezaei4, 5, Author              
Fritzen, Felix1, Author              
Reese, Stefanie6, Author              
Affiliations:
1Institute of Applied Mechanics, University of Stuttgart, Pfaffenwaldring 7, 70569, Stuttgart, Germany, ou_persistent22              
2Cyber-Physical Simulation Group, Department of Mechanical Engineering, Technische Universität Darmstadt, Dolivostraße 15, 64293, Darmstadt, Germany, ou_persistent22              
3Institute of Applied Mechanics, RWTH Aachen University, Aachen, D-52074, Germany, ou_persistent22              
4Material Mechanics, RWTH Aachen University, Schinkelstr. 2, Aachen, Germany, ou_persistent22              
5Microstructure Physics and Alloy Design, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society, Max-Planck-Straße 1, 40237 Düsseldorf, DE, ou_1863381              
6Institute of Applied Mechanics, RWTH Aachen University, Aachen, Germany, ou_persistent22              

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Language(s): eng - English
 Dates: 2020-01-28
 Publication Status: Published in print
 Pages: -
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 Table of Contents: -
 Rev. Method: Peer
 Identifiers: DOI: 10.1186/s40323-019-0138-7
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Title: Advanced Modeling and Simulation in Engineering Sciences
  Abbreviation : Adv. Model. and Simul. in Eng. Sci.
Source Genre: Journal
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Pages: 27 Volume / Issue: 7 Sequence Number: 1 Start / End Page: - Identifier: ISSN: 2213-7467
CoNE: https://pure.mpg.de/cone/journals/resource/2213-7467