AbstractWe propose a novel deep learning architecture for three-dimensional porous media structure reconstruction from two-dimensional slices. A high-level idea is that we fit a distribution on all possible three-dimensional structures of a specific type based on the given dataset of samples. Then, given partial information (central slices) we recover the three-dimensional structure that is built around such slices. Technically, it is implemented as a deep neural network with encoder, generator and discriminator modules. Numerical experiments show that this method gives a good reconstruction in terms of Minkowski functionals.
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