Boosting Techniques for Physics-Based Vortex Detection

Abstract

Robust automated vortex detection algorithms are needed to facilitate the exploration of large-scale turbulent fluid flow simulations. Unfortunately, robust non-local vortex detection algorithms are computationally intractable for large data sets and local algorithms, while computationally tractable, lack robustness. We argue that the deficiencies inherent to the local definitions occur because of two fundamental issues: the lack of a rigorous definition of a vortex and the fact that a vortex is an intrinsically non-local phenomenon. As a first step towards addressing this problem, we demonstrate the use of machine learning techniques to enhance the robustness of local vortex detection algorithms. We motivate the presence of an expert-in-the-loop using empirical results based on machine learning techniques. We employ adaptive boosting to combine a suite of widely used, local vortex detection algorithms, which we term weak classifiers, into a robust compound classifier. Fundamentally, the training phase of the algorithm, in which an expert manually labels small, spatially contiguous regions of the data, incorporates non-local information into the resulting compound classifier. We demonstrate the efficacy of our approach by applying the compound classifier to two data sets obtained from computational fluid dynamical simulations. Our results demonstrate that the compound classifier has a reduced misclassification rate relative to the component classifiers.