Microstructure reconstruction via artificial neural networks: a combination of causal and non-causal approach

Acta Polytechnica CTU Proceedings

MartinDoškář¹  JanZeman¹  KryštofLatka² 

[1] Czech Technical University in Prague, Faculty of Civil Engineering, Department of Mechanics, Thákurova 7, 166 29 Prague 6, Czech Republic;Gymmázium Nový PORG, Pod Krcským lesem 25, 142 00 Prague 4, Czech Republic;

关键词: microstructure reconstruction;    neural network;    causal neighbourhood;    non-causal neighbourhood;   

DOI  :  10.14311/APP.2022.34.0032

来源: DOAJ

【 摘 要 】

We investigate the applicability of artificial neural networks (ANNs) in reconstructing a sample image of a sponge-like microstructure. We propose to reconstruct the image by predicting the phase of the current pixel based on its causal neighbourhood, and subsequently, use a non-causal ANN model to smooth out the reconstructed image as a form of post-processing. We also consider the impacts of different configurations of the ANN model (e.g., the number of densely connected layers, the number of neurons in each layer, the size of both the causal and non-causal neighbourhood) on the models’ predictive abilities quantified by the discrepancy between the spatial statistics of the reference and the reconstructed sample.

【 授权许可】

Unknown