8 papers
Structural and Lagrangian properties of analogue ensembles to characterize multifractality of stochastic processes
Carlos Granero-Belinchon
We present a framework for the scale-invariance characterization of stochastic processes in reconstructed finite-dimensional phase spaces. This framework analyses the structural an…
Impact of geophysical fields on Deep Learning-based Lagrangian drift simulations
Daria Botvynko, Carlos Granero-Belinchon, Simon Van Gennip +2
We assess the influence of different Eulerian geophysical input fields on Lagrangian drift simulations using DriftNet, a learning-based method designed to simulate Lagrangian drift…
Analog-based ensembles to characterize turbulent dynamics from observed data
Carlos Granero-Belinchon
We present a methodology for the study of the dispersion of trajectories of stochastic processes in reconstructed phase spaces from observed data. The methodology allows to find en…
Relationship between unpredictability and intermittency in shell models of turbulence and experiments
Ewen Frogé, Carlos Granero-Belinchon, Stéphane G. Roux +2
We study the predictability of turbulent velocity signals using probabilistic analog-forecasting. Here, predictability is defined by the accuracy of forecasts and the associated un…
Structure functions and flatness of streamwise velocity in a turbulent channel flow
Carlos Granero-Belinchon, Stéphane G. Roux, Nicolas B. Garnier
In this article, we present a multiscale characterization of the streamwise velocity of a turbulent channel flow. We study the 2nd and 4th order structure functions and the flatnes…
Simulation-informed deep learning for enhanced SWOT observations of fine-scale ocean dynamics
Eugenio Cutolo, Carlos Granero-Belinchon, Ptashanna Thiraux +2
Oceanic processes at fine scales are crucial yet difficult to observe accurately due to limitations in satellite and in-situ measurements. The Surface Water and Ocean Topography (S…