Physics-Based Region Clustering to Boost Inference on Computational Fluid Dynamics Flow Fields

The high dimensionality and variability of Computational Fluid Dynamics (CFD) data pose a significant challenge for Machine Learning (ML) models. The only solutions in the literature addressing inference from CFD flow fields are based on expert-driven features, which consist of fluid dynamic quantities averaged on specific regions of the entire computational domain. However, using handcrafted features can limit the scalability and portability of existing methods, and result in the loss of critical flow field information that might be essential for capturing non-linear patterns inherent in the CFD data. We propose a method to replace handcrafted features with features defined on regions obtained by clustering. Our approach combines: i) physics-based clustering, to identify meaningful regions within the flow field, ii) cluster-based feature extraction, to capture localized fluid dynamics properties, and iii) set-learning models to process the extracted information. Our solution allows integrating physics-based modeling with ML, and provides a portable and flexible pipeline capable of effectively dealing with the variability and dimensionality of CFD flow fields. We validate our method on publicly available CFD datasets (from the aerospace domain) and apply it to a realistic scenario, that is, the classification of pathologies in real 3D human upper airways extracted from CT scans, acquired in collaboration with a medical hospital. Experimental results demonstrate the accuracy and scalability of our method, and highlight its potential for leveraging CFD data in ML frameworks for other scientific and engineering applications.