Title: Surrogate models in machine learning for computational stochastic multi-scale modelling in composite materials design

Authors: Bokai Liu; Weizhuo Lu

Addresses: Department of Applied Physics and Electronics, Umeå University, 90187 Umeå, Sweden ' Department of Applied Physics and Electronics, Umeå University, 90187 Umeå, Sweden

Abstract: We propose a computational framework using surrogate models through five steps, which can systematically and comprehensively address a number of related stochastic multi-scale issues in composites design. We then used this framework to conduct an implementation in nano-composite. Uncertain input parameters at different scales are propagated within a bottom-up multi-scale framework. Representative volume elements in the context of finite element modelling (RVE-FEM) are used to finally obtain the homogenised thermal conductivity. The input parameters are selected by a top-down scanning method and subsequently are converted as uncertain inputs. Machine learning approaches are exploited for computational efficiency, where particle swarm optimisation (PSO) and ten-fold cross validation (CV) are employed for hyper-parameter tuning. Our machine learning prediction results agree well with published experimental data, which proves our computational framework can be a versatile and efficient method to design new complex nano-composites.

Keywords: surrogate models; data-driven modelling; DDM; machine learning; stochastic multi-scale modelling; polymeric nanotube composites; PNCs.

DOI: 10.1504/IJHM.2022.127037

International Journal of Hydromechatronics, 2022 Vol.5 No.4, pp.336 - 365

Received: 26 Jan 2022
Accepted: 11 Mar 2022

Published online: 18 Nov 2022 *

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