Physics

Finite element method

Representative elementary volume

Mathematics

Structural engineering

Biology

Algorithm

Composite number

Mathematical optimization

Machine learning

Engineering

Surrogate model

Paleontology

Artificial intelligence

Scale (ratio)

Context (archaeology)

Computer science

Quantum mechanics

Computational model

Particle swarm optimization

International Journal of Hydromechatronics

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.

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