Record link: 
http://hdl.handle.net/20.500.12708/221940
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Title: 
Deep Eshelby Network: an AI framework for multiscale mean-field homogenization with arbitrary inclusion shapes
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Citation: 
Schwaighofer, M., Königsberger, M., Pech, S., Lukacevic, M., & Füssl, J. (2025). Deep Eshelby Network: an AI framework for multiscale mean-field homogenization with arbitrary inclusion shapes. In J. Füssl & M. Lukacevic (Eds.), ECCOMAS Thematic Conference on Computational Methods in Wood Mechanics from Material Properties to Timber Structures : Program & Book of Abstracts (pp. 81–81).
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Publication Type: 
Inproceedings - Abstract Book Contribution
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Language: 
English
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Authors: 
Schwaighofer, Michael 
Königsberger, Markus 
Pech, Sebastian 
Lukacevic, Markus 
Füssl, Josef 
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Organisational Unit: 
E202-02 - Forschungsbereich Struktursimulation und Ingenieurholzbau 
E056-02 - Fachbereich Computational Design (DC:CD) 
E057-16 - Fachbereich Center for Geometry and Computational Design 
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Published in: 
ECCOMAS Thematic Conference on Computational Methods in Wood Mechanics from Material Properties to Timber Structures : Program & Book of Abstracts 
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Date (published): 
Jun-2025
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Event name: 
ECCOMAS Thematic Conference on Computational Methods in Wood Mechanics from Material Properties to Timber Structures (CompWood 2025) 
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Event date: 
4-Jun-2025 - 6-Jun-2025
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Event place: 
Wien, Austria
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Number of Pages: 
1
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Keywords: 
Mean-Field Homogenization; Macroscopic Properties; Eshelby's Solutions
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Research Areas: 
Computational Materials Science: 100%
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Science Branch: 
2030 - Maschinenbau: 40%
2011 - Bauingenieurwesen: 40%
1020 - Informatik: 20%
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Appears in Collections:
Conference Paper

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