Surrogate Model Based on Data-Driven Model Reduction for Inelastic Behavior of Composite Microstructure

On the microscale, most composite materials are composed of heterogeneous materials comprising two or more different phases, such as matrices and inclusions. In addition, composite materials may exhibit high variability, depending on the material and amount of material used. Hence, the effect of mic...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	International journal of aeronautical and space sciences Jg. 24; H. 3; S. 732 - 752
Hauptverfasser:	Kim, Hyejin, Jeong, Inho, Cho, Haeseong, Cho, Maenghyo
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Seoul The Korean Society for Aeronautical & Space Sciences (KSAS) 01.07.2023 Springer Nature B.V 한국항공우주학회
Schlagworte:	Accuracy Aerospace Technology and Astronautics Composite materials Computational efficiency Computing costs Constitutive models Decomposition Deep learning Deformation Engineering Fluid- and Aerodynamics Homogenization Inclusions Microstructure Model reduction Neural networks Original Paper Structural analysis Wavelet transforms 항공우주공학 Deep learning Inelasticity Model order reduction Data-driven Surrogate model
ISSN:	2093-274X, 2093-2480
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!