We adopt convolutional neural networks (CNN) to predict the basic properties
of the porous media. Two different media types are considered: one mimics the
sandstone, 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 system's geometry and 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. The models
predict with reasonable accuracy but only within the data type, they are
trained on. For instance, the model trained on sandstone-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. Moreover, the network trained on one data type works well
for the other. For instance, the model trained on sandstone-like samples works
perfectly on biological-like samples.

本文采用卷积神经网络 (CNN) 来预测多孔介质的基本属性。该文分两项任务，第一项任务是利用几何分析来预测孔隙率和有效扩散系数，第二项任务则是通过重构系统的几何和浓度图来完成。其中，本文提出了两种不同种类的 CNN 模型，分别为 C-Net 和 U-Net。此外，U-Net 能够准确地重构浓度场，而且在各种数据类型上都表现出了良好的性能。