Can I read Deep Learning for Diffusion in Porous Media on EtoBox?

Deep Learning for Diffusion in Porous Media by Graczyk, Krzysztof M.; Strzelczyk, Dawid; Matyka, Maciej is a scholarly article available to read on EtoBox.

What is Deep Learning for Diffusion in Porous Media about?

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 typ

Author

Graczyk, Krzysztof M.; Strzelczyk, Dawid; Matyka, Maciej

Published

2023

Language

EN