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Beyond the generally deployed features for microstructure property predictionthis study aims to improve the machine learned prediction by developing novelfeature descriptors. Therefore, Bayesian infused data mining is conducted toacquire samples containing characteristics inexplicable to the current featureset, and suitable feature descriptors to describe these characteristics areproposed. The iterative development of feature descriptors resulted in 37 novelfeatures, being able to reduce the prediction error by roughly one third. Tofurther improve the predictive model, convolutional neural networks (Conv Nets)are deployed to generate auxiliary features in a supervised machine learningmanner. The Conv Nets were able to outperform the feature based approach. A keyingredient for that is a newly proposed data augmentation scheme and thedevelopment of so-called deep inception modules. A combination of the featurebased approach and the convolutional neural network leads to a hybrid neuralnetwork: A parallel deployment of the both neural network archetypes in asingle model achieved a relative rooted mean squared error below 1%, more thanhalving the error compared to prior models operating on the same data. Thehybrid neural network was found powerful enough to be extended to predictvariable material parameters, from a low to high phase contrast, while allowingfor arbitrary microstructure geometry at the same time.

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