PoreML: A Data-Driven Framework for Learning Multiphase Flow in Porous Media

Key points

• Predicting these flows remains challenging because wettability and complex pore geometry govern the nonlinear evolution of fluid interfaces.1
• To fill this critical gap, we introduce PoreML, an open-source framework unifying data generation, model training, and evaluation grounded in pore-scale physics.1
• The framework comprises three core components. (a) A modern GPU-native lattice Boltzmann solver, validated against analytical solutions and published experiments, enables reproducible data generation. (b) A 3.3 TB dataset contains 560 simulation runs and 158,546 stored time steps across four application-driven scenarios.1
• PoreML provides a shared foundation for machine-learning research on multiphase flow in porous media, with the aim of empowering the community to develop reliable predictive models and advance the field.1

Sources (1)

• [1]PoreML: A Data-Driven Framework for Learning Multiphase Flow in Porous Media
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 7, 04:08 PM

  Multiphase flow in porous microstructures is central to CO$_2$ storage, fuel-cell operation, and flip-chip packaging.

  Predicting these flows remains challenging because wettability and complex pore geometry govern the nonlinear evolution of fluid interfaces.

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