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

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

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

Radia Spiga, Mireille Batton-Hubert, Marianne Sarazin - Sept. 25, 2019
Predicting hospital admissions with integer-valued time series


Nihad Aghbalou, Mohamed Tahar Mabrouk, Pierrick Haurant, Mireille Batton-Hubert, Bruno Lacarrière - Sept. 25, 2019
Metamodeling Based Approach for District Heat Network Aggregation
International Conference on Time Series and Forecasting, ITISE 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

David Gaudrie, Rodolphe Le Riche, Victor Picheny - Sept. 19, 2019
Faster Multi-Objective Optimization: Cumulating Gaussian Processes, Preference Point and Parallelism
19th French-German-Swiss conference on optimization

Rodolphe Le Riche - Sept. 5, 2019
Optimization under uncertainties: an overview with a focus on Gaussian processes


Rodolphe Le Riche, Nicolas Durrande - Sept. 4, 2019
An overview of kriging for researchers


Mireille Batton-Hubert, Eric Desjardin, François Pinet - Sept. 1, 2019
L’imperfection des données géographiques 1 Bases théoriques


Mireille Batton-Hubert, Eric Desjardin, François Pinet - Sept. 1, 2019
Imperfection et information géographique


All publications are here