Detecting Lagrangian coherent structures from sparse and noisy trajectory data

The paper introduces a clear least‑squares estimator with Tikhonov regularization and an empirical DBSCAN pipeline, but it offers no theoretical convergence or clustering‑stability guarantees beyond heuristics for β, δ, and eps plateaus. The model supplies a plausible convergence outline and DBSCAN stability conditions, yet key steps (notably C^2 control for FTLE ridge convergence and a fully explicit sampling/noise analysis) remain unproven, so its argument is also incomplete. See the paper’s formulation and heuristics for the LS solution, FTLE computation, and DBSCAN parameter selection .