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[2602.14108] Geometry-Aware Physics-Informed PointNets for Modeling Flows Across Porous Structures
Summary
This article presents a novel approach using Physics Informed PointNets (PIPN) and Geometry Aware Neural Operators (P-IGANO) to model fluid flows through and around porous structures, demonstrating effective generalization across various geometries.
Why It Matters
The study addresses a significant challenge in fluid dynamics and machine learning by providing a systematic evaluation of advanced neural network models that can predict complex flow behaviors. This has implications for engineering design and environmental modeling, where accurate flow predictions are essential.
Key Takeaways
- Introduces PIPN and P-IGANO for modeling flows in porous structures.
- Demonstrates low error rates in velocity and pressure predictions.
- Validates the models against unseen geometries and boundary conditions.
- Highlights performance degradation near sharp interfaces and large gradients.
- Shows potential for accelerating design studies without retraining.
Computer Science > Machine Learning arXiv:2602.14108 (cs) [Submitted on 15 Feb 2026] Title:Geometry-Aware Physics-Informed PointNets for Modeling Flows Across Porous Structures Authors:Luigi Ciceri, Corrado Mio, Jianyi Lin, Gabriele Gianini View a PDF of the paper titled Geometry-Aware Physics-Informed PointNets for Modeling Flows Across Porous Structures, by Luigi Ciceri and 3 other authors View PDF HTML (experimental) Abstract: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...
