activity
20192022
most citedSurrogate Modeling of Aerodynamic Simulations for Multiple Operating Conditions Using Machine Learning

49 citations · 145 across the 6 of their papers we have counts for

collaborators

7 papers

astro-ph.CO202235 cited

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…

cs.CV2021

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,…

physics.plasm-ph202046 cited

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…

physics.space-ph2020

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…

physics.flu-dyn20191 cited

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…

physics.flu-dyn201949 cited

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…