2023/05/04 by G.D. Marques, Marques, Gil, Marco Martins Afonso +3
Earth and Planetary Sciences · Engineering · Mathematics · #Amplitude #Artificial intelligence #Autocorrelation #Classical mechanics #Computer science #FOS: Mathematics #Fluid Dynamics and Turbulent Flows #Fluid Dynamics and Vibration Analysis #Gaussian #Geology #Geometry #Mathematical analysis #Mathematics #Mechanics #Meteorological Phenomena and Simulations #Noise (video) #Optics #Optimization and Control (math.OC) #Particle (ecology) #Physics #Point (geometry) #Position (finance) #Statistics #Stochastic differential equation #Tracking (education) #Trajectory #Vortex #Vorticity
paper · pdf · doi:10.48550/arxiv.2305.02737
openalex publication_date 2023/05/04 · openalex created_date 2023/05/07 · openalex updated_date 2026/08/04
We present a novel method for estimating the circulations and positions of point vortices using trajectory data of passive particles in the presence of Gaussian noise. The method comprises two algorithms: the first one calculates the vortex circulations, while the second one reconstructs the vortex trajectories. This reconstruction is done thanks to a hierarchy of optimization problems, involving the integration of systems of differential equations, over time sub-intervals all with the same amplitude defined by the autocorrelation function for the advected passive particles' trajectories. Our findings indicate that accurately tracking the position of vortices and determining their circulations is achievable, even when passive particle trajectories are affected by noise.