  Homogenization is a technique commonly used in multiscale computational
science and engineering for predicting collective response of heterogeneous
materials and extracting effective mechanical properties. In this paper, a
three-dimensional deep convolutional neural network (3D-CNN) is proposed to
predict the effective material properties for representative volume elements
(RVEs) with random spherical inclusions. The high-fidelity dataset generated by
a computational homogenization approach is used for training the 3D-CNN models.
The inference results of the trained networks on unseen data indicate that the
network is capable of capturing the microstructural features of RVEs and
produces an accurate prediction of effective stiffness and Poisson's ratio. The
benefits of the 3D-CNN over conventional finite-element-based homogenization
with regard to computational efficiency, uncertainty quantification and model's
transferability are discussed in sequence. We find the salient features of the
3D-CNN approach make it a potentially suitable alternative for facilitating
material design with fast product design iteration and efficient uncertainty
quantification.
