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 two 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

Benjamin Antunes - Nov. 14, 2024
Calcul à hautes performances : Reproductibilité et répétabilité des résultats numériques et des mesures de performances


Benjamin A. Antunes, Claude Mazel, David R.C. Hill - Sept. 30, 2024
Performance and reproducibility assessment of quantum dissipative dynamics framework: a comparative study of Fortran compilers, MKL, and FFTW


Didier Rullière, Marc Grossouvre - Sept. 20, 2024
A Joint Kriging Model with Application to Constrained Classification


Noé Lebreton, Julien Jacques, Julien Ah-Pine, Matthieu Neveu - Sept. 15, 2024
Pattern matching for multivariate time series forecasting
ENBIS-24 Conference

Anis Fradi, Chafik Samir - Aug. 22, 2024
A New Framework for Evaluating the Validity and the Performance of Binary Decisions on Manifold-Valued Data
European Conference (ECML-PKDD)

Benjamin A. Antunes, David R.C. Hill - Aug. 1, 2024
Reproducibility, Replicability, and Repeatability: A survey of reproducible research with a focus on high performance computing
Computer Science Review

Benjamin A. Antunes, David R.C. Hill - July 15, 2024
Recherche Reproductible : Comment les outils informatiques et le calcul scientifique impactent bien des disciplines


Anis Fradi, Chafik Samir, Ines Adouani - July 6, 2024
A New Bayesian Approach to Global Optimization on Parametrized Surfaces in $\mathbb {R}^{3}$
Journal of Optimization Theory and Applications

Julien Ah-Pine - June 24, 2024
Contributions en science des données. Fusion d'informations, fonctions d'agrégation, mesures en clustering, variétés non linéaires et données fonctionnelles


Guillaume Perrin, Rodolphe Le Riche - June 20, 2024
Bayesian optimization with derivatives acceleration
Workshop on Bayesian optimization & related topics

All publications are here