dc.identifier.uri |
http://dx.doi.org/10.15488/14138 |
|
dc.identifier.uri |
https://www.repo.uni-hannover.de/handle/123456789/14252 |
|
dc.contributor.author |
Aldakheel, Fadi
|
|
dc.contributor.author |
Elsayed, Elsayed S.
|
|
dc.contributor.author |
Zohdi, Tarek I.
|
|
dc.contributor.author |
Wriggers, Peter
|
|
dc.date.accessioned |
2023-07-13T07:10:41Z |
|
dc.date.available |
2023-07-13T07:10:41Z |
|
dc.date.issued |
2023 |
|
dc.identifier.citation |
Aldakheel, F.; Elsayed, E.S.; Zohdi, T.I.; Wriggers, P.: Efficient multiscale modeling of heterogeneous materials using deep neural networks. In: Computational Mechanics 72 (2023), Nr. 1, S. 155-171. DOI: https://doi.org/10.1007/s00466-023-02324-9 |
|
dc.description.abstract |
Material modeling using modern numerical methods accelerates the design process and reduces the costs of developing new products. However, for multiscale modeling of heterogeneous materials, the well-established homogenization techniques remain computationally expensive for high accuracy levels. In this contribution, a machine learning approach, convolutional neural networks (CNNs), is proposed as a computationally efficient solution method that is capable of providing a high level of accuracy. In this work, the data-set used for the training process, as well as the numerical tests, consists of artificial/real microstructural images (“input”). Whereas, the output is the homogenized stress of a given representative volume element RVE . The model performance is demonstrated by means of examples and compared with traditional homogenization methods. As the examples illustrate, high accuracy in predicting the homogenized stresses, along with a significant reduction in the computation time, were achieved using the developed CNN model. |
eng |
dc.language.iso |
eng |
|
dc.publisher |
Berlin ; Heidelberg : Springer |
|
dc.relation.ispartofseries |
Computational Mechanics 72 (2023), Nr. 1 |
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dc.rights |
CC BY 4.0 Unported |
|
dc.rights.uri |
https://creativecommons.org/licenses/by/4.0 |
|
dc.subject |
Computational micro-to-macro approach |
eng |
dc.subject |
Convolutional neural networks |
eng |
dc.subject |
Deep learning |
eng |
dc.subject |
Heterogeneous materials |
eng |
dc.subject.ddc |
530 | Physik
|
|
dc.subject.ddc |
004 | Informatik
|
|
dc.title |
Efficient multiscale modeling of heterogeneous materials using deep neural networks |
eng |
dc.type |
Article |
|
dc.type |
Text |
|
dc.relation.essn |
1432-0924 |
|
dc.relation.issn |
0178-7675 |
|
dc.relation.doi |
https://doi.org/10.1007/s00466-023-02324-9 |
|
dc.bibliographicCitation.issue |
1 |
|
dc.bibliographicCitation.volume |
72 |
|
dc.bibliographicCitation.firstPage |
155 |
|
dc.bibliographicCitation.lastPage |
171 |
|
dc.description.version |
publishedVersion |
|
tib.accessRights |
frei zug�nglich |
|