PoreML：一种用于学习多孔介质中多相流的数据驱动框架

中文摘要 AI 辅助

多孔微结构中的多相流是CO$_2$封存、燃料电池运行和倒装芯片封装的核心问题。由于润湿性和复杂的孔隙几何形状控制着流体界面的非线性演化，预测这些流动仍然具有挑战性。机器学习为该领域的发展带来了巨大潜力，但进展受到稀缺的时间分辨三维数据集以及缺乏用于训练和评估模型的统一工作流程的限制。为填补这一关键空白，我们提出了PoreML，一个基于孔隙尺度物理的开源框架，统一了数据生成、模型训练和评估。该框架包含三个核心组件。(a) 一个现代GPU原生格子玻尔兹曼求解器，已针对解析解和已发表的实验进行验证，可实现可复现的数据生成。(b) 一个3.3 TB的数据集，包含560次模拟运行和158,546个存储的时间步，涵盖四个应用驱动的场景。这些轨迹跨越合成结构和从真实材料微CT扫描中获得的几何结构，覆盖了多样的润湿条件和粘度比。(c) 一个统一的学习框架，评估单步预测和自回归展开。其领域特定的评估协议评估预测准确性和物理一致性。我们在这些协议下评估了五种不同架构的模型。两个互补的挑战评估了向更大领域的迁移以及从合成结构到微CT衍生结构的迁移。PoreML为多孔介质中多相流的机器学习研究提供了共享基础，旨在赋能社区开发可靠的预测模型并推动该领域发展。

英文摘要

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.

发表机构

• Imperial College London（帝国理工学院）
• Stanford University（斯坦福大学）
• EarthFlow AI, Inc.（EarthFlow AI公司）
• Queen Mary University of London（伦敦玛丽女王大学）

机构由 AI 辅助整理，请以论文原文为准。