Predicting flows that occur both through and around porous bodies is challenging due to coupled physics across fluid and porous regions and the need to generalize across diverse geometries and boundary conditions. We address this problem using two Physics Informed learning approaches: Physics Informed PointNets (PIPN) and Physics Informed Geometry Aware Neural Operator (P-IGANO). We enforce the incompressible Navier Stokes equations in the free-flow region and a Darcy Forchheimer extension in the porous region within a unified loss and condition the networks on geometry and material parameters. Datasets are generated with OpenFOAM on 2D ducts containing porous obstacles and on 3D windbreak scenarios with tree canopies and buildings. We first verify the pipeline via the method of manufactured solutions, then assess generalization to unseen shapes, and for PI-GANO, to variable boundary conditions and parameter settings. The results show consistently low velocity and pressure errors in both seen and unseen cases, with accurate reproduction of the wake structures. Performance degrades primarily near sharp interfaces and in regions with large gradients. Overall, the study provides a first systematic evaluation of PIPN/PI-GANO for simultaneous through-and-around porous flows and shows their potential to accelerate design studies without retraining per geometry.

翻译：预测同时通过和围绕多孔体的流动具有挑战性，原因在于流体区域与多孔区域之间的耦合物理机制，以及需要泛化至多种几何形状和边界条件。我们采用两种物理信息学习方法解决此问题：物理信息点云网络（PIPN）与物理信息几何感知神经算子（PI-GANO）。我们在自由流动区域强制实施不可压缩纳维-斯托克斯方程，在多孔区域采用达西-福希海默扩展形式，将其统一于损失函数中，并使网络以几何与材料参数为条件。数据集通过OpenFOAM生成，包含二维含多孔障碍物的管道流场景，以及三维含树冠与建筑物的防风林场景。我们首先通过构造解方法验证流程，随后评估对未见几何形状的泛化能力，并对PI-GANO评估其在可变边界条件与参数设置下的表现。结果表明，在已见与未见案例中均保持较低的速度与压力误差，并能准确复现尾流结构。性能下降主要出现在尖锐界面附近及梯度较大区域。总体而言，本研究首次系统评估了PIPN/PI-GANO对多孔体穿透与绕流耦合流动的建模能力，并展示了其在无需针对特定几何形状重新训练的情况下加速设计研究的潜力。