Stochastic interpretable machine learning based multiscale modeling in thermal conductivity of Polymeric graphene-enhanced composites

authored by: Bokai Liu, Weizhuo Lu, Thomas Olofsson, Xiaoying Zhuang, Timon Rabczuk
Abstract: We introduce an interpretable stochastic integrated machine learning based multiscale approach for the prediction of the macroscopic thermal conductivity in Polymeric graphene-enhanced composites (PGECs). This method encompasses the propagation of uncertain input parameters from the meso to macro scale, implemented through a foundational bottom-up multi-scale framework. In this context, Representative Volume Elements in Finite Element Modeling (RVE-FEM) are employed to derive the homogenized thermal conductivity. Besides, we employ two sets of techniques: Regression-tree-based methods (Random Forest and Gradient Boosting Machine) and Neural networks-based approaches (Artificial Neural Networks and Deep Neural Networks). To ascertain the relative influence of factors on output estimations, the SHapley Additive exPlanations (SHAP) algorithm is integrated. This interpretable machine learning methodology demonstrates strong alignment with published experimental data. It holds promise as an efficient and versatile tool for designing new composite materials tailored to applications involving thermal management.
Organisation(s): Institute of Photonics
External Organisation(s): Bauhaus-Universität Weimar
Umea University
Type: Article
Journal: Composite Structures
Volume: 327
No. of pages: 16
ISSN: 0263-8223
Publication date: 01.01.2024
Publication status: Published
Peer reviewed: Yes
ASJC Scopus subject areas: Ceramics and Composites, Civil and Structural Engineering
Electronic version(s): http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-215912 (Access: Open)
https://doi.org/10.1016/j.compstruct.2023.117601 (Access: Closed)
: Details in the research portal "Research@Leibniz University"

BibTeX

@article{a25147c81f93455b9791861efe1ef413,
title = "Stochastic interpretable machine learning based multiscale modeling in thermal conductivity of Polymeric graphene-enhanced composites",
abstract = "We introduce an interpretable stochastic integrated machine learning based multiscale approach for the prediction of the macroscopic thermal conductivity in Polymeric graphene-enhanced composites (PGECs). This method encompasses the propagation of uncertain input parameters from the meso to macro scale, implemented through a foundational bottom-up multi-scale framework. In this context, Representative Volume Elements in Finite Element Modeling (RVE-FEM) are employed to derive the homogenized thermal conductivity. Besides, we employ two sets of techniques: Regression-tree-based methods (Random Forest and Gradient Boosting Machine) and Neural networks-based approaches (Artificial Neural Networks and Deep Neural Networks). To ascertain the relative influence of factors on output estimations, the SHapley Additive exPlanations (SHAP) algorithm is integrated. This interpretable machine learning methodology demonstrates strong alignment with published experimental data. It holds promise as an efficient and versatile tool for designing new composite materials tailored to applications involving thermal management.",
keywords = "Data-driven technique, Interpretable Integrated Learning, Polymeric graphene-enhanced composites (PGECs), Stochastic multi-scale modeling, Thermal properties",
author = "Bokai Liu and Weizhuo Lu and Thomas Olofsson and Xiaoying Zhuang and Timon Rabczuk",
note = "Funding Information: We gratefully acknowledge the support of the EU project H2020-AURORAL Grant agreement ID: 101016854 (Architecture for Unified Regional and Open digital ecosystems for Smart Communities and Rural Areas Large scale application) and the Kempe Foundation Sweden (Kempestiftelserna - Stiftelserna J.C. Kempes och Seth M. Kempes minne). This work is also funded and supported by J. Gust. Richert stiftelse, SWECO, Sweden (Grant agreement ID: 2023–00884). The computations handling were enabled by resources provided by the Swedish National Infrastructure for Computing (SNIC) and Academic Infrastructure for Supercomputing in Sweden (NAISS) at High-Performance Computing Center North (HPC2N) partially funded by the Swedish Research Council through grant agreement no. 2018-05973 and no. 2022–06725.",
year = "2024",
month = jan,
day = "1",
doi = "10.1016/j.compstruct.2023.117601",
language = "English",
volume = "327",
journal = "Composite Structures",
issn = "0263-8223",
publisher = "Elsevier BV",
}

Back