38 citations · 130 across the 15 of their papers we have counts for
18 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…
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…
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…
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…
Land Surface Temperature Super-Resolution with a Scale-Invariance-Free Neural Approach: Application to MODIS
Romuald Ait-Bachir, Carlos Granero-Belinchon, Aurélie Michel +3
Due to the trade-off between the temporal and spatial resolution of thermal spaceborne sensors, super-resolution methods have been developed to provide fine-scale Land SurfaceTempe…
Analog-Based Forecasting of Turbulent Velocity: Relationship between Unpredictability and Intermittency
Ewen Frogé, Carlos Granero-Belinchon, Stéphane G. Roux +2
This study evaluates the performance of analog-based methodologies to predict, in a statistical way, the longitudinal velocity in a turbulent flow. The data used comes from hot wir…
Multiscale and Anisotropic Characterization of Images Based on Complexity: an Application to Turbulence
Carlos Granero-Belinchon, Stéphane G. Roux, Nicolas B. Garnier
This article presents a multiscale, non-linear and directional statistical characterization of images based on the estimation of the skewness, flatness, entropy and distance from G…
Deep learning for Lagrangian drift simulation at the sea surface
Daria Botvynko, Carlos Granero-Belinchon, Simon Van Gennip +2
We address Lagrangian drift simulation in geophysical dynamics and explore deep learning approaches to overcome known limitations of state-of-the-art model-based and Markovian appr…
Neural network based generation of a 1-dimensional stochastic field with turbulent velocity statistics
Carlos Granero-Belinchon
We define and study a fully-convolutional neural network stochastic model, NN-Turb, which generates a 1-dimensional field with some turbulent velocity statistics. In particular, th…
Convolutional Neural Network Modelling for MODIS Land Surface Temperature Super-Resolution
Binh Minh Nguyen, Ganglin Tian, Minh-Triet Vo +3
Nowadays, thermal infrared satellite remote sensors enable to extract very interesting information at large scale, in particular Land Surface Temperature (LST). However such data a…
Quantifying Non-Stationarity with Information Theory
Carlos Granero-Belinchon, Stéphane G. Roux, Nicolas B. Garnier
We introduce an index based on information theory to quantify the stationarity of a stochastic process.The index compares on the one hand the information contained in the increment…
Information theory for non-stationary processes with stationary increments
Carlos Granero-Belinchon, Stéphane G. Roux, Nicolas Garnier
We describe how to analyze the wide class of non stationary processes with stationary centered increments using Shannon information theory. To do so, we use a practical viewpoint a…
Probing high order dependencies with information theory
C Granero-Belinchón, S. Roux, P. Abry +1
Information theoretic measures (entropies, entropy rates, mutual information) are nowadays commonly used in statistical signal processing for real-world data analysis. The present…
Information Theory to probe Intrapartum Fetal Heart Rate Dynamics
Carlos Granero-Belinchon, Stéphane Roux, Patrice Abry +3
Intrapartum fetal heart rate (FHR) monitoring constitutes a reference tool in clinical practice to assess the baby health status and to detect fetal acidosis. It is usually analyze…
A Kullback-Leibler divergence measure of intermittency: application to turbulence
Carlos Granero-Belinchon, Stephane G. Roux, Nicolas B. Garnier
For generic systems exhibiting power law behaviors, and hence multiscale dependencies, we propose a new, and yet simple, tool to analyze multifractality and intermittency, after no…
Scaling of Information in Turbulence
Carlos Granero-Belinchon, Stephane G. Roux, Nicolas B. Garnier
We propose a new perspective on Turbulence using Information Theory. We compute the entropy rate of a turbulent velocity signal and we particularly focus on its dependence on the s…