<div class="csl-bib-body">
<div class="csl-entry">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.), <i>ECCOMAS Thematic Conference on Computational Methods in Wood Mechanics from Material Properties to Timber Structures : Program & Book of Abstracts</i> (pp. 81–81).</div>
</div>
-
dc.identifier.uri
http://hdl.handle.net/20.500.12708/221940
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dc.language.iso
en
-
dc.subject
Mean-Field Homogenization
en
dc.subject
Macroscopic Properties
en
dc.subject
Eshelby's Solutions
en
dc.title
Deep Eshelby Network: an AI framework for multiscale mean-field homogenization with arbitrary inclusion shapes
en
dc.type
Inproceedings
en
dc.type
Konferenzbeitrag
de
dc.description.startpage
81
-
dc.description.endpage
81
-
dc.type.category
Abstract Book Contribution
-
tuw.booktitle
ECCOMAS Thematic Conference on Computational Methods in Wood Mechanics from Material Properties to Timber Structures : Program & Book of Abstracts
-
tuw.researchTopic.id
C1
-
tuw.researchTopic.name
Computational Materials Science
-
tuw.researchTopic.value
100
-
tuw.publication.orgunit
E202-02 - Forschungsbereich Struktursimulation und Ingenieurholzbau
