Feature Extraction from Flow Fields: Physics-Based Clustering and Morphing with Applications

The high dimensionality of flow fields obtained from Computational Fluid Dynamics (CFD) poses major challenges for Machine Learning (ML), especially when the scarcity of training data combines with strong geometric variability. Most existing ML approaches for inference from CFD data rely on expert-defined features, primarily quantities computed over manually selected regions. However, this strategy does not scale well, since regions must be redefined for each new geometry, requiring expert knowledge and significant effort. To overcome this limitation, we introduce two complementary methods to extract features from CFD flow fields: the first identifies meaningful flow regions by clustering features derived from the governing equations; the second employs mesh morphing to align each flow field onto a common reference geometry, enabling consistent use of expert-defined regions across cases. Both require minimal human intervention on new samples and ensure scalability across diverse CFD scenarios. We validate our methods on two distinct applications: first, by accurately identifying airfoil shapes and geometric defects; second, by classifying nasal pathologies from 3D CFD simulations of human upper airways reconstructed from CT scans. Both methods show robustness and high accuracy, highlighting their potential for automated, generalizable, and scalable CFD analysis within ML frameworks.