PORE 2-3 Machine Learning application in Porous Media: from image processing to multiscale models

Join us from June 24th to July 3rd for, "PORE 2-3 Machine Learning application in Porous Media: from image processing to multiscale models" which will be taught by Gege Wen, Assistant Professor at Imperial College London, co-appointed by Earth Science Engineering and I-X (Imperial AI).

This course will explore 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.

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.

The course will consist of four lectures hosted on Tuesdays and Thursdays from 18:00-21:00 British Summer Time: June 24th, June 26th, July 1st, & July 3rd.