Theme Metamodeling

Presentation

The MOCA team studies the management of numerical simulation models and their use in continuous optimization. Metamodeling encompasses here the simulation of physical systems and the two types of metamodels (models of models) :

    • Statistical metamodels (e.g., Gaussian processes) build from input-output data. The data often comes itself from the simulation of physical systems (e.g., finite elements models). We specialize in statistical metamodels built from sparse data. Such metamodels typically underly optimization algorithms.

    • Metamodels that aim at producing computer programs by describing and processing a class of models. Such metamodels may also be metacodes that algorithmically describe a set of possible program instances.

The group has contributions in domains such as continuous optimization theory and computer experiments. In addition, the group implements simulation softwares for high-performance computing, discrete events simulation software, and focuses on the numerical reproductibility of the results.

 

Keywords:

  • Statistical models
  • Continuous optimization
  • Discrete event simulation
  • Scientific Computing
  • High Performance Computing
  • Numerical reproductibility

Last publications

Rodolphe Le Riche, Adrien Spagnol, David Gaudrie, Sébastien da Veiga, Victor Picheny - July 9, 2020
Reducing dimension in Bayesian Optimization
LIMOS internal seminar

Nicolas Wagner, Violaine Antoine, Jonas Koko, Romain Lardy - June 15, 2020
Fuzzy k-NN Based Classifiers for Time Series with Soft Labels
18. International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems

Alexis Pereda, David R.C. Hill, Claude Mazel, Loïc Yon, Bruno Bachelet - Feb. 19, 2020
Processing Algorithmic Skeletons at Compile-Time
21ème congrès de la société française de Recherche Opérationnelle et d'Aide à la Décision (ROADEF)

Rodolphe Le Riche, Nicolas Garland, Yann Richet, Nicolas Durrande - Jan. 1, 2020
Multi-fidelity for MDO using Gaussian Processes


Stéphanie Mahévas, Victor Picheny, Patrick Lambert, Nicolas Dumoulin, Lauriane Rouan, Christophe Soulié, Dimo Brockhoff, Sigrid Lehuta, Rodolphe Le Riche, Robert Faivre, Hilaire Drouineau - Dec. 19, 2019
A practical guide for conducting calibration and decision-making optimisation with complex ecological models


David Gaudrie, Rodolphe Le Riche, Victor Picheny, Benoit Enaux, Vincent Herbert - Dec. 3, 2019
Bayesian Optimization in Reduced Eigenbases
PGMO Days 2019

Mohamed Reda El Amri, Christophette Blanchet-Scalliet, Celine Helbert, Rodolphe Le Riche - Dec. 3, 2019
Bayesian Optimization Under Uncertainty for Chance Constrained Problems
PGMO Days 2019

Alexis Pereda, David R.C. Hill, Claude Mazel, Bruno Bachelet - Nov. 28, 2019
Algorithmic Skeletons Using Template Metaprogramming
14th International Student Conference on Advanced Science and Technology (ICAST)

David Gaudrie, Rodolphe Le Riche, Victor Picheny, Benoit Enaux, Vincent Herbert - Nov. 27, 2019
Budgeted Bayesian Multiobjective Optimization
Journées de la Chaire Oquaido 2019

Olivier Roustant, Michel Lutz - Sept. 25, 2019
Un exemple de compétition pédagogique en science des données
Colloque francophone international sur l'enseignement de la statistique

All publications are here