425 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Centre National de la Recherche ScientifiqueFR43 papers
- EDF Energy (United Kingdom)GB24 papers
- CERMICSFR17 papers
- Université Paris-SaclayFR17 papers
- Institut de Mathématiques de ToulouseFR16 papers
- Commissariat à l'Énergie Atomique et aux Énergies AlternativesFR14 papers
- Institut national de recherche en sciences et technologies du numériqueFR14 papers
- Laboratoire de Probabilités, Statistique et ModélisationFR14 papers
- Université Toulouse III - Paul SabatierFR13 papers
- Centre de Mathématiques Appliquées de l'École polytechniqueFR12 papers
- École PolytechniqueFR12 papers
- Laboratoire de Probabilités et Modèles AléatoiresFR12 papers
17 papers · 1 filter
Qualifying quantum approaches for hard industrial optimization problems. A case study in the field of smart-charging of electric vehicles
Constantin Dalyac, Loïc Henriet, Emmanuel Jeandel +4
In order to qualify quantum algorithms for industrial NP-Hard problems, comparing them to available polynomial approximate classical algorithms and not only to exact ones -- expone…
Interoperability and computational framework for simulating open channel hydraulics: application to sensitivity analysis and calibration of Gironde Estuary model
Cedric Goeury, Yoann Audouin, Fabrice Zaoui
Water resource management is of crucial societal and economic importance, requiring a strong capacity for anticipating environmental change. Progress in physical process knowledge,…
Hierarchical coupled routing-charging model of electric vehicles, stations and grid operators
Benoît Sohet, Yezekael Hayel, Olivier Beaude +1
Electric Vehicles' (EVs) growing number has various consequences, from reducing greenhouse gas emissions and local pollution to altering traffic congestion and electricity consumpt…
Augmenting Physical Models with Deep Networks for Complex Dynamics Forecasting
Yuan Yin, Vincent Le Guen, Jérémie Dona +4
Forecasting complex dynamical phenomena in settings where only partial knowledge of their dynamics is available is a prevalent problem across various scientific fields. While purel…
Decomposition of convex high dimensional aggregative stochasticcontrol problems
Adrien Séguret, Clémence Alasseur, J. Frédéric Bonnans +3
We consider the framework of convex high dimensional stochastic control problems, in which the controls are aggregated in the cost function. As first contribution, we introduce a m…
An information geometry approach for robustness analysis in uncertainty quantification of computer codes
Clement Gauchy, Jerome Stenger, Roman Sueur +1
Robustness analysis is an emerging field in the domain of uncertainty quantification. It consists of analysing the response of a computer model with uncertain inputs to the perturb…
Training design fostering the emergence of new meanings toward unprecedented and critical events
Simon Flandin, Deli Salini, Artémis Drakos +1
Our research is part of a technological research program in adult education conducted in reference to the "course of action" program. Using the activity-sign hypothesis of this pro…
Logarithmic regret for episodic continuous-time linear-quadratic reinforcement learning over a finite-time horizon
Matteo Basei, Xin Guo, Anran Hu +1
We study finite-time horizon continuous-time linear-quadratic reinforcement learning problems in an episodic setting, where both the state and control coefficients are unknown to t…
Approximation error analysis of some deep backward schemes for nonlinear PDEs
Maximilien Germain, Huyen Pham, Xavier Warin
Recently proposed numerical algorithms for solving high-dimensional nonlinear partial differential equations (PDEs) based on neural networks have shown their remarkable performance…
Fast multivariate empirical cumulative distribution function with connection to kernel density estimation
Nicolas Langrené, Xavier Warin
This paper revisits the problem of computing empirical cumulative distribution functions (ECDF) efficiently on large, multivariate datasets. Computing an ECDF at one evaluation poi…
Reinforcement Learning for Variable Selection in a Branch and Bound Algorithm
Marc Etheve, Zacharie Alès, Côme Bissuel +2
Mixed integer linear programs are commonly solved by Branch and Bound algorithms. A key factor of the efficiency of the most successful commercial solvers is their fine-tuned heuri…
Physically interpretable machine learning algorithm on multidimensional non-linear fields
Rem-Sophia Mouradi, Cédric Goeury, Olivier Thual +2
In an ever-increasing interest for Machine Learning (ML) and a favorable data development context, we here propose an original methodology for data-based prediction of two-dimensio…
Various Ways to Quantify BDMPs
Marc Bouissou, Shahid Khan, Joost-Pieter Katoen +1
A Boolean logic driven Markov process (BDMP) is a dependability analysis model that defines a continuous-time Markov chain (CTMC). This formalism has high expressive power, yet it…
Kalman Recursions Aggregated Online
Eric Adjakossa, Yannig Goude, Olivier Wintenberger
In this article, we aim at improving the prediction of expert aggregation by using the underlying properties of the models that provide expert predictions. We restrict ourselves to…
A Visual Sensitivity Analysis for Parameter-Augmented Ensembles of Curves
Alejandro Ribes, Joachim Pouderoux, Bertrand Iooss
Engineers and computational scientists often study the behavior of their simulations by repeated solutions with variations in their parameters, which can be for instance boundary v…
Discretization and Machine Learning Approximation of BSDEs with a Constraint on the Gains-Process
Idris Kharroubi, Thomas Lim, Xavier Warin
We study the approximation of backward stochastic differential equations (BSDEs for short) with a constraint on the gains process. We first discretize the constraint by applying a…
A graph clustering approach to localization for adaptive covariance tuning in data assimilation based on state-observation mapping
Sibo Cheng, Jean-Philippe Argaud, Bertrand Iooss +2
An original graph clustering approach to efficient localization of error covariances is proposed within an ensemble-variational data assimilation framework. Here the localization t…