Record link: 
http://hdl.handle.net/20.500.12708/221939
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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 95th Annual Meeting of the Association of Applied Mathematics and Mechanics : Book of Abstracts (pp. 206–207).
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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 
E057-16 - Fachbereich Center for Geometry and Computational Design 
E056-02 - Fachbereich Computational Design (DC:CD) 
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Published in: 
95th Annual Meeting of the Association of Applied Mathematics and Mechanics : Book of Abstracts 
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ISBN: 
978-83-7775-791-8
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Date (published): 
Apr-2025
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Event name: 
95th Annual Meeting of the International Association of Applied Mathematics and Mechanics (GAMM 2025) 
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Event date: 
7-Apr-2025 - 11-Apr-2025
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Event place: 
Poznań, Poland
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Number of Pages: 
2
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Keywords: 
Eigenstrain concentration; Arbitrary inclosion Shapes
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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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