Record link: 
http://hdl.handle.net/20.500.12708/229392
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Title: 
Elasto-Damage Response of Anisotropic Materials – Machine Learning Based Constitutive Modeling
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Citation: 
Kofler, M., Peyker, L., Luxner, M. H., & Pettermann, H. E. (2026). Elasto-Damage Response of Anisotropic Materials – Machine Learning Based Constitutive Modeling. In 20th European Mechanics of Materials Conferences – EMMC20. 20th European Mechanics of Materials Conferences – EMMC20, Florence, Italy.
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Publication Type: 
Inproceedings - Abstract Book Contribution
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Language: 
English
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Authors: 
Kofler, Michael 
Peyker, Lukas 
Luxner, Mathias H. 
Pettermann, Heinz E. 
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Organisational Unit: 
E317-01-2 - Forschungsgruppe Struktur- und Werkstoffsimulation 
E317-01-1 - Forschungsgruppe Numerische Analyse- und Designmethoden 
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Published in: 
20th European Mechanics of Materials Conferences – EMMC20 
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Date (published): 
2026
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Event name: 
20th European Mechanics of Materials Conferences – EMMC20 
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Event date: 
27-May-2026 - 29-May-2026
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Event place: 
Florence, Italy
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Number of Pages: 
2
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Keywords: 
machine learning; surrogate constitutive model; continuum damage mechanics; composites; wood
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Project title: 
KI-basierte Materialmodellierung für Holzwerkstoff - Phase 2: 0 (Luxner Engineering ZT GmbH) 
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Research Areas: 
Composite Materials: 40%
Modeling and Simulation: 30%
Computational Materials Science: 30%
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Science Branch: 
2030 - Maschinenbau: 40%
2050 - Werkstofftechnik: 30%
2119 - Sonstige Technische Wissenschaften: 30%
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Appears in Collections:
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