arXiv：cs.LG· Chunyang Wang, Mingrui Zhang, Yuyan Zhang, Linqi Zhu, Xin Ju, Edo Sicco Boek, Martin J. Blunt, Gege Wen·· 4 小时前AI 评分27
PoreML：面向多孔介质多相流学习的开源框架
PoreML: A Data-Driven Framework for Learning Multiphase Flow in Porous Media
AI 导读
PoreML 是一个开源框架，统一了多孔介质多相流的数据生成、模型训练与评估，包含 GPU 原生格子 Boltzmann 求解器、3.3 TB 数据集（560 次模拟、158,546 个时间步）和统一的评估协议。该框架在四种应用场景下评估了五种不同架构的模型，并设置了两项迁移挑战：扩展至更大区域以及从合成结构迁移到 micro-CT 扫描结构。
正文
Abstract: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 constrained by scarce time-resolved 3D datasets and a lack of a unified workflow for training and evaluating models. To fill this critical 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 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. 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 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 complementary challenges assess 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.
| Subjects: | Machine Learning (cs.LG); Fluid Dynamics (physics.flu-dyn) |
| Cite as: | arXiv:2610.10314 [cs.LG] |
| (or arXiv:2610.10314v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.10314 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Chunyang Wang [view email]
[v1]
Wed, 7 Oct 2026 16:08:35 UTC (43,660 KB)
来源：arXiv：cs.LG · arxiv.org