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.

翻译：超越微观结构属性预测中普遍采用的特征描述方法，本研究旨在通过开发新特征描述符来提升机器学习预测性能。为此，我们实施贝叶斯启发的数据挖掘，获取包含当前特征集无法解释特性的样本，并提出能描述这些特性的特征描述符。经过迭代开发，共获得37个新特征，使预测误差降低约三分之一。为进一步改进预测模型，我们采用卷积神经网络（Conv Nets）以监督学习方式生成辅助特征。卷积神经网络在性能上超越了基于特征的方法，其关键在于新提出的数据增强方案及所谓深度初始模块的开发。将基于特征的方法与卷积神经网络相结合，形成了混合神经网络：在单一模型中并行部署这两种神经网络原型，实现了相对均方根误差低于1%，较先前基于相同数据的模型误差降低一半以上。该混合神经网络展现出强大扩展能力，可预测从低相衬度到高相衬度的可变材料参数，同时允许任意微观结构几何形状。