  In the past several years, convolutional neural networks (CNNs) have proven
their capability to predict characteristic quantities in porous media research
directly from pore-space geometries. Due to the frequently observed significant
reduction in computation time in comparison to classical computational methods,
bulk parameter prediction via CNNs is especially compelling, e.g. for effective
diffusion. While the current literature is mainly focused on fully saturated
porous media, the partially saturated case is also of high interest. Due to the
qualitatively different and more complex geometries of the domain available for
diffusive transport present in this case, standard CNNs tend to lose robustness
and accuracy with lower saturation rates. In this paper, we demonstrate the
ability of CNNs to perform predictions of relative diffusion directly from full
pore-space geometries. As such, our CNN conveniently fuses diffusion prediction
and a well-established morphological model which describes phase distributions
in partially saturated porous media.
