Record link: 
http://hdl.handle.net/20.500.12708/221164
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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, November 7). Deep Eshelby Network: An AI Framework for Multiscale Mean-Field Homogenization with Arbitrary Inclusion Shapes [Presentation]. 3rd Early Career Scientist Training Workshop on “Building Materials and Engineering Structures,” Wien, Austria. http://hdl.handle.net/20.500.12708/221164
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Publication Type: 
Presentation - Presentation
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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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Date (published): 
7-Nov-2025
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Event name: 
3rd Early Career Scientist Training Workshop on “Building Materials and Engineering Structures” 
en
Event date: 
7-Nov-2025
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Event place: 
Wien, Austria
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Keywords: 
Continuum micromechanics homogenization; Supersphere inclusion shape
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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:
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