Inferring Functional Properties from Fluid Dynamics Features

In a wide range of applied problems involving fluid flows, Computational Fluid Dynamics (CFD) provides detailed quantitative information on the flow field, at variable level of fidelity and computational cost. However, CFD alone cannot predict high-level functional properties that are not easily obtained from the equations of fluid motion. In this work, we present a data-driven framework to extract these additional information, such as medical diagnostic output, from CFD solutions. This is a challenging task because of the huge data dimensionality of CFD, and the limited training data that can be typically gathered due to the large computational cost of CFD. By pursuing a traditional Machine Learning (ML) pipeline of pre-processing, feature extraction, and model training, we demonstrate that informative features can be extracted from CFD data. Two experiments, pertaining to different application domains, support our claim that the convective properties implicit into a CFD solution can be leveraged to retrieve functional information that does not admit an analytical definition. Despite the preliminary nature of our study and the relative simplicity of both the geometrical and CFD models, for the first time we demonstrate that the combination of ML and CFD can diagnose a complex system in terms of high-level functional properties.