A Transfer Learning Approach for Microstructure Reconstruction and Structure-property Predictions.

ABSTRACT: Stochastic microstructure reconstruction has become an indispensable part of computational materials science, but ongoing developments are specific to particular material systems. In this paper, we address this generality problem by presenting a transfer learning-based approach for microstructure reconstruction and structure-property predictions that is applicable to a wide range of material systems. The proposed approach incorporates an encoder-decoder process and feature-matching optimization using a deep convolutional network. For microstructure reconstruction, model pruning is implemented in order to study the correlation between the microstructural features and hierarchical layers within the deep convolutional network. Knowledge obtained in model pruning is then leveraged in the devel

SUBMITTER: Li X 

PROVIDER: S-EPMC6128837 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature