TY - JOUR

T1 - Stochastic integrated machine learning based multiscale approach for the prediction of the thermal conductivity in carbon nanotube reinforced polymeric composites

AU - Liu, Bokai

AU - Vu-Bac, Nam

AU - Zhuang, Xiaoying

AU - Fu, Xiaolong

AU - Rabczuk, Timon

N1 - Funding Information: We gratefully acknowledge the support of the China Scholarship Council (CSC) .

PY - 2022/6/16

Y1 - 2022/6/16

N2 - 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.

AB - 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.

KW - Carbon nanotube reinforced polymeric composites (CNT-PCs)

KW - Computational complexity

KW - Machine learning

KW - Multi-scale stochastic modeling

KW - Thermal properties

UR - http://www.scopus.com/inward/record.url?scp=85130118707&partnerID=8YFLogxK

U2 - 10.1016/j.compscitech.2022.109425

DO - 10.1016/j.compscitech.2022.109425

M3 - Article

AN - SCOPUS:85130118707

VL - 224

JO - Composites science and technology

JF - Composites science and technology

SN - 0266-3538

M1 - 109425

ER -