Hybrid machine-learned homogenization: Bayesian data mining and
convolutional neural networks
- URL: http://arxiv.org/abs/2302.12545v1
- Date: Fri, 24 Feb 2023 09:59:29 GMT
- Title: Hybrid machine-learned homogenization: Bayesian data mining and
convolutional neural networks
- Authors: Julian Li{\ss}ner and Felix Fritzen
- Abstract summary: This study aims to improve the machine learned prediction by developing novel feature descriptors.
The iterative development of feature descriptors resulted in 37 novel features, being able to reduce the prediction error by roughly one third.
A combination of the feature based approach and the convolutional neural network leads to a hybrid neural network.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
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