CNN-based prediction of microstructure-derived random property fields of composite materials

Author:

Gavallas PanagiotisORCID,Stefanou GeorgeORCID,Savvas DimitriosORCID,Mattrand CécileORCID,Bourinet Jean-Marc

Funder

Horizon 2020 Framework Programme

H2020 Marie Skłodowska-Curie Actions

Publisher

Elsevier BV

Reference42 articles.

1. Characterization of random composites using moving-window technique;Baxter;J. Eng. Mech.,2000

2. Determination of RVE size for random composites with local volume fraction variation;Savvas;Comput. Methods Appl. Mech. Engrg.,2016

3. Stochastic finite element analysis of composite structures based on mesoscale random fields of material properties;Stefanou;Comput. Methods Appl. Mech. Engrg.,2017

4. Efficient quantification of composite spatial variability: A multiscale framework that captures intercorrelation;Van Bavel;Compos. Struct.,2023

5. ImageNet classification with deep convolutional neural networks;Krizhevsky;Neural Inf. Process. Syst.,2012

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