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Data-Driven Microstructure Property Relations

Efficient Methods for Mechanical Analysis, Institute of Applied Mechanics (CE), University of Stuttgart, 70569 Stuttgart, Germany
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Math. Comput. Appl. 2019, 24(2), 57; https://doi.org/10.3390/mca24020057
Received: 26 March 2019 / Revised: 24 May 2019 / Accepted: 28 May 2019 / Published: 31 May 2019
An image based prediction of the effective heat conductivity for highly heterogeneous microstructured materials is presented. The synthetic materials under consideration show different inclusion morphology, orientation, volume fraction and topology. The prediction of the effective property is made exclusively based on image data with the main emphasis being put on the 2-point spatial correlation function. This task is implemented using both unsupervised and supervised machine learning methods. First, a snapshot proper orthogonal decomposition (POD) is used to analyze big sets of random microstructures and, thereafter, to compress significant characteristics of the microstructure into a low-dimensional feature vector. In order to manage the related amount of data and computations, three different incremental snapshot POD methods are proposed. In the second step, the obtained feature vector is used to predict the effective material property by using feed forward neural networks. Numerical examples regarding the incremental basis identification and the prediction accuracy of the approach are presented. A Python code illustrating the application of the surrogate is freely available. View Full-Text
Keywords: microstructure property linkage; unsupervised machine learning; supervised machine learning; neural network; snapshot proper orthogonal decomposition microstructure property linkage; unsupervised machine learning; supervised machine learning; neural network; snapshot proper orthogonal decomposition
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Lißner, J.; Fritzen, F. Data-Driven Microstructure Property Relations. Math. Comput. Appl. 2019, 24, 57.

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