- Titel
- FE-NN
- Untertitel
- Efficient-scale transition for heterogeneous microstructures using neural networks
- Zitierfähige Url:
- https://nbn-resolving.org/urn:nbn:de:bsz:14-qucosa2-951746
- Quellenangabe
- Proceedings in applied mathematics and mechanics
Erscheinungsjahr: 2023
Jahrgang: 23
Heft: 3
E-ISSN: 1617-7061
Artikelnummer: e202300011 - Erstveröffentlichung
- 2023
- Abstract (EN)
- Numerical modeling and optimization of advanced composite materials can require huge computational effort when considering their heterogeneous mesostructure and interactions between different material phases within the framework of multiscale modeling. Employing machine learning methods for computational homogenization enables the reduction of computational effort for the evaluation of the mesostructural behavior while retaining high accuracy. Classically, one unit cell with representative characteristics of the material is chosen for the description of the heterogeneous structure, which presents a simplification of the actual composite. This contribution presents a neural network-based approach for computational homogenization of composite materials with the ability to consider arbitrary compositions of the mesostructure. Therefore, various statistical volume elements and their respective constitutive responses are evaluated. Thereby, the naturally occurring fluctuation within the composition of the phases can be considered. Different approaches using distinct metrics to represent the arbitrary mesostructures are investigated in terms of required computational effort and accuracy.
- Andere Ausgabe
- Link zum Artikel, der zuerst in der Zeitschrift 'Proceedings in applied mathematics and mechanics” im Wiley-VCH Verlag erschienen ist.
DOI: 10.1002/pamm.202300011 - Freie Schlagwörter (EN)
- Numerical modeling, Advanced composite materials, Machine learning methods, Computational homogenization, Neural network-based approach
- Klassifikation (DDC)
- 510
- Verlag
- Wiley-VCH, Weinheim
- Förder- / Projektangaben
- Deutsche Forschungsgemeinschaft (DFG)
Impaktsicherheit von Baukonstruktionen durch mineralisch gebundene Komposite
(GRK 2250/2, Project B3)
ID: 287321140 - Version / Begutachtungsstatus
- publizierte Version / Verlagsversion
- URN Qucosa
- urn:nbn:de:bsz:14-qucosa2-951746
- Veröffentlichungsdatum Qucosa
- 16.05.2025
- Dokumenttyp
- Artikel
- Sprache des Dokumentes
- Englisch
- Lizenz / Rechtehinweis
CC BY 4.0