Stochastic integrated machine learning based multiscale approach for the prediction of the thermal conductivity in carbon nanotube reinforced polymeric composites

We present a stochastic integrated machine learning based multiscale approach for the prediction of the macroscopic thermal conductivity in carbon nanotube reinforced polymeric composites (CNT-PCs). Seven types of machine learning models are exploited, namely Multivariate Adaptive Regression Splines...

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Bibliographic Details
Published in:	Composites science and technology Vol. 224; p. 109425
Main Authors:	Liu, Bokai, Vu-Bac, Nam, Zhuang, Xiaoying, Fu, Xiaolong, Rabczuk, Timon
Format:	Journal Article
Language:	English
Published:	Barking Elsevier Ltd 16.06.2022 Elsevier BV
Subjects:	Carbon Carbon nanotube reinforced polymeric composites (CNT-PCs) Carbon nanotubes Composite materials Computational complexity Computing costs Cost analysis Heat conductivity Heat transfer Machine learning Multi-scale stochastic modeling Multiscale analysis Nanotubes Particle swarm optimization Regression analysis Stochastic models Support vector machines Thermal conductivity Thermal management Thermal properties Thermal properties Carbon nanotube reinforced polymeric composites (CNT-PCs) Multi-scale stochastic modeling Computational complexity Machine learning
ISSN:	0266-3538, 1879-1050
Online Access:	Get full text
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