Accurately predicting transport properties of porous fibrous materials by machine learning methods

Cawte, Taylr; Bazylak, Aimy

doi:10.1002/elsa.202100185

Cited by 5 publications

(1 citation statement)

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“…An example of the usage of CNNs to calculate both permeability and tortuosity is given by Graczyk and Matyka 21 . In Cawte and Bazylak 22 , diffusion and permeability are studied within machine learning tools, including gradient boosting regression, neural network, and support vector regression. Image-based prediction of effective diffusion, D, was performed for scanning electron microscope data 23 and random field-based porous media 24 .…”

Section: Openmentioning

confidence: 99%

Deep learning for diffusion in porous media

Graczyk

Strzelczyk

Matyka

2023

Sci Rep

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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 et al. in 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.

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