Course fee depends on your InterPore account type
PORE 2-3 Machine Learning application in Porous Media: from image processing to multiscale models
This course explores the intersection of machine learning and porous media research, providing participants with theoretical foundations and practical applications. Students will learn how AI models can accelerate porous media analysis through advanced image processing techniques, including segmentation, super-resolution, and synthetic image generation. The course covers parameter estimation from imaging data and dynamic modeling of transport phenomena in complex porous structures.
What Will You Learn?
Key machine learning architectures covered include Convolutional Neural Networks (CNN) for image analysis, Graph Neural Networks (GNN) for structural representation, Physics-Informed Neural Networks (PINN) for incorporating physical constraints, and generative AI architectures.
