  We adopt convolutional neural networks (CNN) to predict the basic properties
of the porous media. Two different media types are considered: one mimics the
sand packings, and the other mimics the systems derived from the extracellular
space of biological tissues. The Lattice Boltzmann Method is used to obtain the
labeled data necessary for performing supervised learning. We distinguish two
tasks. In the first, networks based on the analysis of the system's geometry
predict porosity and effective diffusion coefficient. In the second, networks
reconstruct the concentration map. In the first task, we propose two types of
CNN models: the C-Net and the encoder part of the U-Net. Both networks are
modified by adding a self-normalization module [Graczyk \textit{et al.}, Sci
Rep 12, 10583 (2022)]. The models predict with reasonable accuracy but only
within the data type, they are trained on. For instance, the model trained on
sand packings-like samples overshoots or undershoots for biological-like
samples. In the second task, we propose the usage of the U-Net architecture. It
accurately reconstructs the concentration fields. In contrast to the first
task, the network trained on one data type works well for the other. For
instance, the model trained on sand packings-like samples works perfectly on
biological-like samples. Eventually, for both types of the data, we fit
exponents in the Archie's law to find tortuosity that is used to describe the
dependence of the effective diffusion on porosity.
