Hybrid machine-learned homogenization: Bayesian data mining and convolutional neural networks

TL;DR

This paper introduces a hybrid machine learning approach combining Bayesian data mining, novel feature descriptors, and convolutional neural networks to significantly improve microstructure property prediction accuracy.

Contribution

It develops 37 new feature descriptors through Bayesian data mining and integrates CNN-generated features into a hybrid model, reducing prediction error substantially.

Findings

Reduced prediction error by roughly one third with new features.

Hybrid model achieved less than 1% relative RMSE, halving previous errors.

Extended to predict variable material parameters with arbitrary microstructure geometry.

Abstract

Beyond the generally deployed features for microstructure property prediction this study aims to improve the machine learned prediction by developing novel feature descriptors. Therefore, Bayesian infused data mining is conducted to acquire samples containing characteristics inexplicable to the current feature set, and suitable feature descriptors to describe these characteristics are proposed. The iterative development of feature descriptors resulted in 37 novel features, being able to reduce the prediction error by roughly one third. To further improve the predictive model, convolutional neural networks (Conv Nets) are deployed to generate auxiliary features in a supervised machine learning manner. The Conv Nets were able to outperform the feature based approach. A key ingredient for that is a newly proposed data augmentation scheme and the development of so-called deep inception modules. A combination of the feature based approach and the convolutional neural network leads to a hybrid neural network: A parallel deployment of the both neural network archetypes in a single model achieved a relative rooted mean squared error below 1%, more than halving the error compared to prior models operating on the same data. The hybrid neural network was found powerful enough to be extended to predict variable material parameters, from a low to high phase contrast, while allowing for arbitrary microstructure geometry at the same time.