Processing Uncertain Microstructural Data (PN3-1)

Machine Learning for Microstructure Property Relationships based on Images
[Photo: Felix Fritzen, Julian Lißner]

From microstructural images to effective material properties

Feature extraction based on 2-PCF

While machine learning today relies heavily on convolutional neural networks when it comes to image analysis, the two-point correlation functions (2-PCF) can complement them efficiently: Analytical homogenization methods are often based on (partial) information on the two-point statistics which emphasizes its relevance for the effective microstructure -property relationship.

We account for the 2-PCF by studying a large variety of random microstructures and extracting the dominant information by means of an incremental snapshot proper orthogonal decomposition (POD)/principal component analysis (PCA). The resulting reduced parameters are used as input features for subsequent learning tasks alongside, e.g., the phase volume fractions of the material contrast.

Incremental POD of the 2-PCF of microstructures (more details: follow link)

Link to publication

Funding information and acknowledgement

 SimTech Logo  Funded by Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy - EXC 2075 - 390740016. We acknowledge the support by the Stuttgart Center for Simulation Science (SC SimTech).
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