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Found 4 papers, 1 papers with code
Machine Learning in Heterogeneous Porous Materials
The "Workshop on Machine learning in heterogeneous porous materials" brought together international scientific communities of applied mathematics, porous media, and material sciences with experts in the areas of heterogeneous materials, machine learning (ML) and applied mathematics to identify how ML can advance materials research.
Multi-Scale Neural Networks for to Fluid Flow in 3D Porous Media
The permeability of complex porous materials can be obtained via direct flow simulation, which provides the most accurate results, but is very computationally expensive.
MudrockNet: Semantic Segmentation of Mudrock SEM Images through Deep Learning
Segmentation and analysis of individual pores and grains of mudrocks from scanning electron microscope images is non-trivial because of noise, imaging artifacts, variation in pixel grayscale values across images, and overlaps in grayscale values among different physical features such as silt grains, clay grains, and pores in an image, which make their identification difficult.
Modeling nanoconfinement effects using active learning
At this scale, the fluid properties are affected by nanoconfinement effects due to the increased fluid-solid interactions.
