A multiscale analysis-assisted two-stage reduced-order deep learning approach for effective thermal conductivity of arbitrary contrast heterogeneous materials-Reference-Cited by-同舟云学术

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A multiscale analysis-assisted two-stage reduced-order deep learning approach for effective thermal conductivity of arbitrary contrast heterogeneous materials

Published:2024-10 Issue: Volume:136 Page:108916
ISSN:0952-1976
Container-title:Engineering Applications of Artificial Intelligence
language:en
Short-container-title:Engineering Applications of Artificial Intelligence

Author:

Yang Zihao,Wu Xixin,He Xindang,Guan Xiaofei

Funder

Chinese Academy of Sciences

Natural Science Foundation of Chongqing Municipality

National Key Research and Development Program of China

Natural Science Foundation of Shanghai Municipality

Natural Science Foundation of Shaanxi Province

Publisher

Elsevier BV

Reference56 articles.

1. Prediction and optimization of mechanical properties of composites using convolutional neural networks;Abueidda;Compos. Struct.,2019

2. Micromechanical finite element predictions of a reduced coefficient of thermal expansion for 3d periodic architectured interpenetrating phase composites;Abueidda;Compos. Struct.,2015

3. Anon,, 0000. https://keras.io/.

4. Homogenization method for effective thermal conductivity of metal hydride bed;Asakuma;Int. J. Hydrogen. Energy,2004

5. Support Vector Regression;Awad,2015

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