PoreML: A Data-Driven Framework for Learning Multiphase Flow in Porous Media
PoreML is an open-source framework that unifies data generation, model training, and evaluation for learning multiphase flow in porous media. It introduces a validated Lattice Boltzmann solver to create a massive dataset of 560 runs and 158,546 time steps across diverse geometries. The framework uses advanced metrics like the Euler characteristic to rigorously test machine learning models for physical accuracy.