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

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. Machine learning holds substantial promise for advancing the field, but progress is hindered by scarce time-resolved 3D datasets and a lack of a unified workflow for training and evaluating models. To fill this gap, we introduce **PoreML**, an open-source framework unifying data generation, model training, and evaluation grounded in pore-scale physics. The framework comprises three core components. **(a)** A validated GPU-native [lattice Boltzmann solver](https://github.com/PoreML/bob) for multiphase flow in complex geometries supports reproducible data generation and future dataset expansion. **(b)** A [**3.3 TB dataset**](https://huggingface.co/datasets/PoreML/PoreML_data) contains **560 simulation runs** and **158,546 stored time steps** across four application-driven scenarios. These trajectories span synthetic structures and geometries derived from micro-CT scans of real materials, covering diverse wetting conditions and viscosity ratios. **(c)** A unified [learning framework](https://github.com/PoreML/poreml) evaluates one-step prediction and autoregressive rollouts. Its domain-specific evaluation protocols assess predictive accuracy and physical consistency. We evaluate five models of diverse architecture under these protocols. Two targeted challenges assess models’ suitability for practically relevant settings by testing transfer to larger domains and from synthetic to micro-CT-derived structures. 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.