Theme Team Metamodeling, Continuous Optimization and Applications (MOCA)

Presentation

The MOCA team studies the management of numerical simulation models and their use in continuous optimization. Metamodeling encompasses here ttwo types of "models of models" :

    • Statistical metamodels (e.g., Gaussian processes) that are built from input-output data. The data is often generated by the simulation of physical systems (e.g., with finite elements models). We specialize in statistical metamodels that are compatible with 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 team 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 and numerical reproductibility.

 

Keywords:

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

Last publications

Radia Spiga, François-Elie Calvier, Anne Carricajo, Bruno Pozzetto, Béatrice Trombert-Paviot, Cédric Bousquet - May 27, 2022
Automated Coding in Case Mix Databases of Bacterial Infections Based on Antimicrobial Susceptibility Test Results
32nd Medical Informatics Europe Conference, MIE2022

Rodolphe Le Riche, David Gaudrie, Victor Picheny, youssef Diouane, Adrien Spagnol, Sébastien da Veiga - May 16, 2022
Which Gaussian Process for Bayesian Optimization ?
2022 Optimization Days

Julien Pelamatti, Rodolphe Le Riche, Céline Helbert, Christophette Blanchet-Scalliet - May 2, 2022
Coupling and selecting constraints in Bayesian optimization under uncertainties


Hassan Hamie, Anis Hoayek, Bassam El-Ghoul, Mahmoud Khalifeh - May 1, 2022
Application of non-parametric statistical methods to predict pumpability of geopolymers for well cementing
Journal of Petroleum Science and Engineering

Rodolphe Le Riche - April 28, 2022
De l'optimisation à l'IA pour l'identification : un voyage à travers les small data
Modélisation et simulation numérique des matériaux

Julien Pelamatti, Mohamed Reda El Amri, Rodolphe Le Riche, Christophette Blanchet-Scalliet, Céline Helbert - April 12, 2022
Sampling Criteria for Constrained Bayesian Optimization under Uncertainty
Hybrid Conference: 2022 SIAM Conference on Uncertainty Quantification

Laurence Grammont, Xavier Bay, Hassan Maatouk - March 30, 2022
Optimal Smoothing and Gaussian Processes with noisy data under constraints


Mireille Batton-Hubert, Eric Desjardin, François Pinet - March 21, 2022
Incertitude épistémique : des données aux modèles en géomatique
Journées MAGIS 2022

Rodolphe Le Riche, David Gaudrie, Jhouben Cuesta-Ramirez, Sami Ben Elhaj Salah, Olivier Roustant, Victor Picheny, Nicolas Durrande, Xavier Bay, Julien Bruchon, Nicolas Moulin - March 10, 2022
A discussion of the current and potential uses of Gaussian Processes in mechanics
Journée scientifique de la SF2M: matériau numérique

Hassan Maatouk, Xavier Bay, Didier Rullière - Feb. 19, 2022
A note on simulating hyperplane-truncated multivariate normal distributions


All publications are here