Geometry-Aware Physics-Informed PointNets for Modeling Flows Across Porous Structures
This study introduces and evaluates two physics-informed deep learning frameworks, PIPN and P-IGANO, which successfully model coupled fluid-porous flows across diverse and unseen geometries by enforcing Navier-Stokes and Darcy-Forchheimer equations within a unified loss function, thereby offering a retraining-free approach to accelerate design studies despite minor performance degradation near sharp interfaces.