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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| Veröffentlicht in: | Composites science and technology Jg. 224; S. 109425 |
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| Format: | Journal Article |
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Elsevier Ltd
16.06.2022
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| ISSN: | 0266-3538, 1879-1050 |
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| Abstract | 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 (MARS), Support Vector Machine (SVM), Regression Tree (RT), Bagging Tree (Bag), Random Forest (RF), Gradient Boosting Machine (GBM) and Cubist. They are used as components of stochastic modeling constructing the relationship between all uncertain inputs variables and the output of interest, the macroscopic thermal conductivity of the composite. Particle Swarm Optimization (PSO) is used for hyper-parameter tuning to find the global optimal values leading to a significant reduction in the computational cost. We also analyze the advantages and disadvantages of various methods in terms of computational expense and model complexity. We believe that the presented stochastic integrated machine learning approach accounting for uncertainties is a valuable step towards computational design of new composites for application related to thermal management.
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| AbstractList | 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 (MARS), Support Vector Machine (SVM), Regression Tree (RT), Bagging Tree (Bag), Random Forest (RF), Gradient Boosting Machine (GBM) and Cubist. They are used as components of stochastic modeling constructing the relationship between all uncertain inputs variables and the output of interest, the macroscopic thermal conductivity of the composite. Particle Swarm Optimization (PSO) is used for hyper-parameter tuning to find the global optimal values leading to a significant reduction in the computational cost. We also analyze the advantages and disadvantages of various methods in terms of computational expense and model complexity. We believe that the presented stochastic integrated machine learning approach accounting for uncertainties is a valuable step towards computational design of new composites for application related to thermal management.
[Display omitted] 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 (MARS), Support Vector Machine (SVM), Regression Tree (RT), Bagging Tree (Bag), Random Forest (RF), Gradient Boosting Machine (GBM) and Cubist. They are used as components of stochastic modeling constructing the relationship between all uncertain inputs variables and the output of interest, the macroscopic thermal conductivity of the composite. Particle Swarm Optimization (PSO) is used for hyper-parameter tuning to find the global optimal values leading to a significant reduction in the computational cost. We also analyze the advantages and disadvantages of various methods in terms of computational expense and model complexity. We believe that the presented stochastic integrated machine learning approach accounting for uncertainties is a valuable step towards computational design of new composites for application related to thermal management. |
| ArticleNumber | 109425 |
| Author | Vu-Bac, Nam Zhuang, Xiaoying Liu, Bokai Rabczuk, Timon Fu, Xiaolong |
| Author_xml | – sequence: 1 givenname: Bokai surname: Liu fullname: Liu, Bokai organization: Institute of Structural Mechanics, Bauhaus-Universität Weimar, Marienstr. 15, D-99423, Weimar, Germany – sequence: 2 givenname: Nam surname: Vu-Bac fullname: Vu-Bac, Nam organization: Institute of Photonics, Gottfried Wilhelm Leibniz Universität Hannover, 30167, Hannover, Germany – sequence: 3 givenname: Xiaoying surname: Zhuang fullname: Zhuang, Xiaoying organization: Institute of Photonics, Gottfried Wilhelm Leibniz Universität Hannover, 30167, Hannover, Germany – sequence: 4 givenname: Xiaolong surname: Fu fullname: Fu, Xiaolong organization: Xi'an Modern Chemistry Research Institute, Xi'an, 710065, China – sequence: 5 givenname: Timon surname: Rabczuk fullname: Rabczuk, Timon email: timon.rabczuk@uni-weimar.de organization: Institute of Structural Mechanics, Bauhaus-Universität Weimar, Marienstr. 15, D-99423, Weimar, Germany |
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| Keywords | Thermal properties Carbon nanotube reinforced polymeric composites (CNT-PCs) Multi-scale stochastic modeling Computational complexity Machine learning |
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| SubjectTerms | 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 |
| Title | Stochastic integrated machine learning based multiscale approach for the prediction of the thermal conductivity in carbon nanotube reinforced polymeric composites |
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