Topological Data Analysis combined with Machine Learning for Predicting Permeability of Porous Media

ArXi:2605.17581v1 Announce Type: cross Flow in porous media is difficult to address using standard analytical or numerical methods due to its complexity. However, since synthetic representations of porous media are easy to produce and data from physical experiments are becoming widely available, the problem is well-suited to studies that include machine learning (ML) techniques. We discuss a number of features that can be extracted from such data, and their utility as input variables into a standard ML algorithm.