2020
DOI: 10.48550/arxiv.2005.03759
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DeePore: a deep learning workflow for rapid and comprehensive characterization of porous materials

Arash Rabbani,
Masoud Babaei,
Reza Shams
et al.

Abstract: DeePore is a deep learning workflow for rapid estimation of a wide range of porous material properties based on the binarized micro-tomography images. We generated 17700 semi-real 3-D micro-structures of porous geo-materials and 30 physical properties of each sample are calculated using physical simulations on the corresponding pore network models. The dataset of porous material images obtained and physical features of them are unprecedented in terms of the number of samples and variety of the extracted featur… Show more

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