# Estimating relative diffusion from 3D micro-CT images using CNNs

arXiv: https://arxiv.org/abs/2208.03337 · HTML: https://mlsanity.com/paper/2208.03337/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 32 references that are in the map.
- First posted on arXiv 4 Aug 2022.

## Builds on (most cited first, 4 of 4)

- [Predicting Porosity, Permeability, and Tortuosity of Porous Media from Images by Deep Learning](https://mlsanity.com/paper/2007.02820/) (2020)
- [Predicting Effective Diffusivity of Porous Media from Images by Deep Learning](https://mlsanity.com/paper/1912.05532/) (2019)
- [Estimating permeability of 3D micro-CT images by physics-informed CNNs based on DNS](https://mlsanity.com/paper/2109.01818/) (2021)
- [Driving Digital Rock towards Machine Learning: predicting permeability with Gradient Boosting and Deep Neural Networks](https://mlsanity.com/paper/1803.00758/) (2018)

## Built on by (most cited first, 1 of 1)

- [Physics-Informed Computer Vision: A Review and Perspectives](https://mlsanity.com/paper/2305.18035/) (2023)

## Abstract (arXiv)

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.