49 citations
- IRT M2PFR4 papers
- Centre National de la Recherche ScientifiqueFR2 papers
- Institut de Mathématiques de ToulouseFR2 papers
- Institut de Recherche en Informatique de ToulouseFR2 papers
- Aix-Marseille UniversitéFR1 paper
- Brown UniversityUS1 paper
- Centre Européen de Recherche et de Formation Avancée en Calcul ScientifiqueFR1 paper
- Dassault Systèmes (France)FR1 paper
- Dassault Systèmes (United States)US1 paper
- École Centrale de LilleFR1 paper
- Florence (Netherlands)NL1 paper
- Laboratoire de Mécanique, Multiphysique, MultiéchelleFR1 paper
8 papers
On-Board Anomaly Detection for Efficient Marine Environmental Monitoring
Thomas Goudemant, Clotilde Szywala, Benjamin Francesconi +4
Marine ecosystems are impacted by various threats such as oil spills, algal blooms, and sediment floods, which disrupt habitats, wildlife, and human activities. Advances in satelli…
On the implementation in Abaqus of the global--local iterative coupling and acceleration techniques
Omar Bettinotti, Stéphane Guinard, Eric Véron +1
This paper presents results and convergence study of the Global--Local Iterative Coupling through the implementation in the commercial software Abaqus making use of the co-simulati…
Confident Object Detection via Conformal Prediction and Conformal Risk Control: an Application to Railway Signaling
Léo Andéol, Thomas Fel, Florence De Grancey +1
Deploying deep learning models in real-world certified systems requires the ability to provide confidence estimates that accurately reflect their uncertainty. In this paper, we dem…
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…
Conservative set valued fields, automatic differentiation, stochastic gradient method and deep learning
Jérôme Bolte, Edouard Pauwels
Modern problems in AI or in numerical analysis require nonsmooth approaches with a flexible calculus. We introduce generalized derivatives called conservative fields for which we d…
Learning Disentangled Representations of Satellite Image Time Series
Eduardo Sanchez, Mathieu Serrurier, Mathias Ortner
In this paper, we investigate how to learn a suitable representation of satellite image time series in an unsupervised manner by leveraging large amounts of unlabeled data. Additio…