Record link: 
http://hdl.handle.net/20.500.12708/227969
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Title: 
A Deep Learning-Enhanced Continuum Micromechanics Framework for Nonlinear Homogeization of Composites with Nonellipsoidal Inclusions Geometries
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Citation: 
Schwaighofer, M., Königsberger, M., Lukacevic, M., & Füssl, J. (2026). A Deep Learning-Enhanced Continuum Micromechanics Framework for Nonlinear Homogeization of Composites with Nonellipsoidal Inclusions Geometries. In 96th Annual Meeting of the Intertnational Association of Applied Mathematics and Mechanics : March16th - 20th, 2026 Stuttgart (Germany) : Book of Abstracts (pp. 221–221). http://hdl.handle.net/20.500.12708/227969
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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 
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 
E056-28 - Fachbereich Computational Sustainability 
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Published in: 
96th Annual Meeting of the Intertnational Association of Applied Mathematics and Mechanics : March16th - 20th, 2026 Stuttgart (Germany) : Book of Abstracts 
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Date (published): 
Mar-2026
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Event name: 
96th Annual Meeting of the International Association of Applied Mathematics and Mechanics (GAMM 2026) 
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Event date: 
16-Mar-2026 - 20-Mar-2026
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Event place: 
Stuttgart, Germany
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Number of Pages: 
1
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Keywords: 
Macroscopic Mechanical Response; Finite Element-based Eshelby Analysis; Nonellipsoidal Inclusions Geometries
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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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