Detecting Lagrangian coherent structures from sparse and noisy trajectory data

Many complex flows such as those arising from ocean plastics in geophysics or moving cells biology are characterized by sparse and noisy trajectory datasets. We introduce techniques for identifying Lagrangian Coherent Structures (LCSs) of hyperbolic elliptic nature Hyperbolic LCSs, which represent surfaces with maximal attraction repulsion over a finite amount time, computed through regularized least-squares approximation the flow map gradient. Elliptic identify regions coherent motion vortices jets, extracted using DBSCAN - popular data clustering algorithm combined systematic approach to choose parameters. deploy these methods on various benchmark analytical real-life experimental datasets ranging oceanography show that they yield accurate results, despite data. also provide lightweight computational implementation user-friendly straightforward Python code.