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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Vydané v:	Composites science and technology Ročník 224; s. 109425
Hlavní autori:	Liu, Bokai, Vu-Bac, Nam, Zhuang, Xiaoying, Fu, Xiaolong, Rabczuk, Timon
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Barking Elsevier Ltd 16.06.2022 Elsevier BV
Predmet:	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
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