Commitees & GDR Partners (2012 - 2015)
- Administrative rattachments
- GDR Coordinators
- Communication committee
- Scientific committee
- Organizing committee
- Working group coordinators
- GDR Partners
You can also see the commitees and partners of the GDR from 2008 to 2011.
Administrative rattachments
The GDR MASCOT-NUM is a CNRS research entity (France), emanating from the INSMI (Mathematical sciences and its interactions) institute.
GDR Coordinators
- Clémentine Prieur (Université Joseph Fourier, Grenoble, France)
- Bertrand Iooss (Electricité de France, R&D, Chatou, France)
- Fabien Mangeant (EADS IW, Suresnes, France)
Communication committee
- Julien Bect (SUPELEC, France)
- Bertrand Iooss (EDF R&D Chatou, France)
- Alexandre Janon (Université Paris-Sud, France)
- Nathalie Saint Geours (Irstea, Montpellier, France)
Scientific committee
- Jean-Marc Azaïs (Université Paul Sabatier, Toulouse, France)
- Gérard Biau (Université Paris VI, France)
- Pierre Del Moral (University of New South Wales, Sydney, Australia)
- Fabrice Gamboa (Université Paul Sabatier, Toulouse, France)
- Josselin Garnier (Université Paris VII, France)
- Olivier Le Maître (CNRS)
- Clémentine Prieur (Université Joseph Fourier, Grenoble, France)
- Luc Pronzato (I3S, CNRS Sophia Antipolis, France)
- Christophe Prud'hommme (Université de Strasbourg, France)
- Bruno Sudret (ETH Zürich)
- Emmanuel Vazquez (SUPELEC, Gif sur Yvette, France)
Organizing committee
- Sébastien Da Veiga (Safran, France)
- Bertrand Iooss (EDF R&D, Chatou, France)
- Fabien Mangeant (EADS IW, France)
- Amandine Marrel (CEA Cadarache, France)
- Hervé Monod (INRA Jouy-en-Josas, Département MIA, France)
- Miguel Munoz-Zuniga (IFPEnergiesnouvelles, Rueil-Malmaison, France)
- Anthony Nouy (Ecole Centrale de Nantes, France)
- Clémentine Prieur (Université Joseph Fourier, Grenoble, France)
- Luc Pronzato (I3S, CNRS Sophia Antipolis, France)
- Olivier Roustant (École des Mines de Saint Étienne, France)
- Nathalie Saint Geours (Irstea, Montpellier, France)
- François Wahl (IFPEnergiesnouvelles, Solaize, France)
Working group coordinators
- Design of numerical experiments: Luc Pronzato & Emmanuel Vazquez
The extensive use of numerical simulations opens the ways for new types of experimentations and designs of experiments: more intensive explorations become possible, more hazardous configurations can be tested and, hopefully, better understanding and optimized responses can be achieved together with more accurate statements about risk and failures. At the same time, complex simulations require long computations, which sets a limitation on what can be learnt in reasonable time.
The domain of design and analysis of computer experiments aims at defining what should be chosen for the inputs of a numerical model in order to achieve a prescribed objective. In particular, one may want to:
(i) predict the behavior of a numerical model from the results of a small number of runs;
(ii) optimize the response of a numerical model; that is, determine the values of inputs corresponding, for example, to the highest performance or smallest cost;
(iii) estimate the variability of a response as a function of that of the inputs (also known as sensitivity analysis);
(iv) estimate a probability of failure in presence of uncertainties when some inputs are randomized with a given probability measure.
Whereas space-filling designs are commonly used for the first objective, different types of designs may be more relevant in other situations. Sequential strategies (or active learning) that construct a model of the numerical simulator step by step, are especially attractive. The topics considered in this Working Group cover the definition of design criteria related to a given objective, the construction of efficient algorithms for the determination of optimal experiments, the investigation of asymptotic properties of designs, the construction of designs for dealing with simulators with several levels of predictive accuracy. Experiments for real physical systems, where in general purely random errors corrupt the observations, are also considered.</sub>
- Uncertainty and sensitivity analyses: Fabrice Gamboa & Bruno Sudret
Usually, deterministic tools of differential calculus are used.
For example, a basic index is obtained via the derivative of one output respective to one imput.
Such approach remains local because derivatives are generally computed at specific points.
In this deterministic approach, we are interested by the automatic differentiation tools which aim at efficiently compute complex code derivatives.
The stochastic approach of uncertainty analysis aims at studying global criteria based on joint pdf modelisation of the problem variables.
The obtained sensitivity indices describe the global variabilities of the phenomena.
For example, the Sobol sensitivity index is given by the ratio between the ouput variance conditionally to one input and the total output variance.
Computation of such quantities leads to very interesting statistical problems that we propose to study.
For example, the efficient estimation of sensitivity indices from a few runs relates to semi or non-parametric estimation techniques.
The stochastic modelisation of the input/output relation ship is another solution.
We can look for models with specific properties (parcimonious representation using ad hoc response surfaces, having remarkable algebraic properties as orthogonality, etc).</sub>
- Computer code approximation via metamodels: Anthony Nouy & Josselin Garnier
- Probabilistic models: Erick Herbin & Ahmed Kebaier
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- Industrial problems: Fabien Mangeant & Mark Asch
Within this group, the first goal is to guarantee the applicability and the transfer of the previous scientific techniques to current difficulties encountered by industrial practitioners. On the other hand, the different industrial partners will propose current challenges to the scientific community. This concerns for example: high dimensionality, computational times, coupling between different codes, scarcity of observational data, special repartition in the space of the factors.
Workshops and dedicated sessions will be organised to ease a fruitful dialog between the different communities.</sub>
- Environmental problems: Clémentine Prieur & Hervé Monod
A number of specific features may arise in such applications :
(i) A fundamental aspect of many geophysical phenomena is the strong interactions between scales (spatial and temporal), and the associated cascade of energy, which of course complicates their modelling.
(ii) An additional source of complexity may arise when the stochastic nature of some parts of the systems cannot be neglected, in particular when rare and extreme events must be detected and allowed for.
(iii) Most of the time, the model is discretized over a huge grid (sometimes with millions of points). We need therefore to perform efficient model reduction methods, in particular to be able to implement a sensitivity analysis on these models.
(iv) Moreover, forecasting systems often combine different sources of information (numerical model, direct observations, statistics, images...) with the help of data assimilation techniques. Such techniques improve the forecasting skill of the system, but make it more complex to analyze.
(v) Performing and studying models which take into account the spatio-temporal dynamic of the phenomena is of great importance. Such models often involve functional inputs and/or outputs. Various methods should be developed in that sense.
The development of efficient methods to study environmental problems requires taking these features into account, a goal which implies pluridisciplinarity. A key aspect of this working group is namely to gather specialists from different domains, coming either from the applied mathematics world (numerical tools, stochastic point of view, data base management), or from the many fields of expertise associated with environment applications (in particular geophysicists, biophysicists, hydrologists, ...).</sub>
- Software aspects: Christophe Prud'homme & Anne Dutfoy
GDR Partners
- Établissements publics (EPIC et EPA)
- CEA (Commissariat à l'Energie Atomique)
- CNRM (Centre National de Recherches Météorologiques)
- IFP Énergies nouvelles
- Lyon, département Mathématiques appliquées
- IFREMER (Institut Francais de Recherche pour l'Exploration de la Mer)
- IRSN (Institut de Radioprotection et de Sûreté Nucléaire)
- Fontenay-aux-Roses
- Cadarache (DPAM/SEMIC/LIMSI)
- ONERA (Office National d'Etudes et Recherches Aérospatiales)
- Laboratoires publics
- Institut Camille Jordan de Lyon
- Laboratoire Jean Kuntzmann (Grenoble)
- Institut de Mathématiques de Toulouse
- Institut Mathématiques et Modélisation - Université Montpellier 2
- Statistique Analyse Modélisation Multidisciplinaire (SAMM) - Université Paris 1
- Laboratoire MAS (Mathématiques appliquées aux systèmes) - Ecole Centrale Paris
- Institut de Mathématiques de Bordeaux
- Laboratoire en Sciences et Technologies de l’Information, Equipe CROCUS (Calcul de Risque, Optimisation et Calage par l'Utilisation de Simulateurs) - Ecole des Mines de St-Etienne
- Laboratoire Informatique, Signaux et Systèmes de Sophia-Antipolis (I3S)
- Département Signaux & Systèmes Electroniques (SSE), E3S (Equipe Supélec Sciences des Systèmes) - SUPELEC
- Département MIA (Mathématique et Informatique Appliquées) - Institut National de Recherche Agronomique (INRA)
- Unité MIA Toulouse
- Unité MIA Jouy
- Unité BIA Toulouse
- Département BioSP - Institut National de Recherche Agronomique (INRA)
- Laboratoire de Mécanique de Rouen
- Laboratoire de Statistique Théorique et Appliquée - Université Pierre et Marie Curie
- Laboratoire Amiénois des Mathématiques Fondamentales et appliquées (LAMFA) - Université de Picardie
- Laboratoire J. A. Dieudonné - Université de Nice Sophia Antipolis
- Institut de Recherche en Génie et Mécanique (GeM) - Ecole Centrale de Nantes
- Laboratoire de l'Informatique du Parallélisme - Ecole Normale Supérieure de Lyon
- Companies