research
∙
06/04/2023
How neural networks learn to classify chaotic time series
Neural networks are increasingly employed to model, analyze and control ...
share
research
∙
02/18/2022
Towards a Numerical Proof of Turbulence Closure
The development of turbulence closure models, parametrizing the influenc...
research
∙
08/26/2021
Benchmarking high-fidelity pedestrian tracking systems for research, real-time monitoring and crowd control
High-fidelity pedestrian tracking in real-life conditions has been an im...
research
∙
03/31/2020
Controlling Rayleigh-Bénard convection via Reinforcement Learning
Thermal convection is ubiquitous in nature as well as in many industrial...
research
∙
01/14/2020
Pedestrian orientation dynamics from high-fidelity measurements
We investigate in real-life conditions and with very high accuracy the d...
research
∙
11/19/2019
Measurement and analysis of visitors' trajectories in crowded museums
We tackle the issue of measuring and analyzing the visitors' dynamics in...
research
∙
11/13/2019
Deep learning velocity signals allows to quantify turbulence intensity
Turbulence, the ubiquitous and chaotic state of fluid motions, is charac...
research
∙
02/07/2019
StampNet: unsupervised multi-class object discovery
Unsupervised object discovery in images involves uncovering recurring pa...
research
∙
05/31/2018
Accurate pedestrian localization in overhead depth images via Height-Augmented HOG
We tackle the challenge of reliably and automatically localizing pedestr...
research
∙
06/09/2017
