49 citations · 204 across the 10 of their papers we have counts for
10 papers
A Deep Learning Approach to Infer Galaxy Cluster Masses from Planck Compton parameter maps
Daniel de Andres, Weiguang Cui, Florian Ruppin +8
Galaxy clusters are useful laboratories to investigate the evolution of the Universe, and accurately measuring their total masses allows us to constrain important cosmological para…
The Three Hundred project: A Machine Learning method to infer clusters of galaxies mass radial profiles from mock Sunyaev-Zel'dovich maps
A. Ferragamo, D. de Andres, A. Sbriglio +9
We develop a machine learning algorithm to infer the 3D cumulative radial profiles of total and gas mass in galaxy clusters from thermal Sunyaev-Zel'dovich effect maps. We generate…
Mass Estimation of Planck Galaxy Clusters using Deep Learning
Daniel de Andres, Weiguang Cui, Florian Ruppin +6
Clusters of galaxies mass can be inferred by indirect observations, see X-ray band, Sunyaev-Zeldovich (SZ) effect signal or optical. Unfortunately, all of them are affected by some…
Unsupervised classification of simulated magnetospheric regions
Maria Elena Innocenti, Jorge Amaya, Joachim Raeder +3
In magnetospheric missions, burst mode data sampling should be triggered in the presence of processes of scientific or operational interest. We present an unsupervised classificati…
A Framework using Contrastive Learning for Classification with Noisy Labels
Madalina Ciortan, Romain Dupuis, Thomas Peel
We propose a framework using contrastive learning as a pre-training task to perform image classification in the presence of noisy labels. Recent strategies such as pseudo-labeling,…
Automatic classification of plasma regions in near-Earth space with supervised machine learning: application to Magnetospheric Multi Scale 2016-2019 observations
Hugo Breuillard, Romain Dupuis, Alessandro Retino +3
The proper classification of plasma regions in near-Earth space is crucial to perform unambiguous statistical studies of fundamental plasma processes such as shocks, magnetic recon…
Visualizing and Interpreting Unsupervised Solar Wind Classifications
Jorge Amaya, Romain Dupuis, Maria Elena Innocenti +1
One of the goals of machine learning is to eliminate tedious and arduous repetitive work. The manual and semi-automatic classification of millions of hours of solar wind data from…
Improved Surrogate Modeling using Machine Learning for Industrial Civil Aircraft Aerodynamics
Romain Dupuis, Jean-Christophe Jouhaud, Pierre Sagaut
Predicting and simulating aerodynamic fields for civil aircraft over wide flight envelopes represent a real challenge mainly due to significant numerical costs and complex flows. S…
Surrogate Modeling of Aerodynamic Simulations for Multiple Operating Conditions Using Machine Learning
Romain Dupuis, Jean-Christophe Jouhaud, Pierre Sagaut
This article presents an original methodology for the prediction of steady turbulent aerodynamic fields. Due to the important computational cost of high-fidelity aerodynamic simula…
Characterizing magnetic reconnection regions using Gaussian mixture models on particle velocity distributions
Romain Dupuis, Martin V. Goldman, David L. Newman +2
We present a method based on unsupervised machine learning to identify regions of interest using particle velocity distributions as a signature pattern. An automatic density estima…