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

Anis Fradi, Chafik Samir - Nov. 10, 2024
Learning and Regression on the Grassmannian


Tanguy Appriou, Didier Rullière, David Gaudrie - Feb. 26, 2024
High-dimensional Bayesian Optimization with a Combination of Kriging models


Benjamin A. Antunes, David R.C. Hill - Feb. 10, 2024
Reproducibility, Replicability, and Repeatability: A survey of reproducible research with a focus on high performance computing


Benjamin A. Antunes, David R. C Hill - Feb. 2, 2024
Reproducibility, energy efficiency and performance of pseudorandom number generators in machine learning: a comparative study of python, numpy, tensorflow, and pytorch implementations


Marc Grossouvre, Didier Rullière, Jonathan Villot - Jan. 1, 2024
Predicting missing Energy Performance Certificates: Spatial interpolation of mixture distributions
Energy and IA

Benjamin A. Antunes, David Hill - Dec. 29, 2023
Evaluating Simultaneous Multi-threading and Affinity Performance for Reproducible Parallel Stochastic Simulation
Research Reports on Computer Science

David Gaudrie, Rodolphe Le Riche, Victor Picheny - Dec. 21, 2023
Modeling and Optimization with Gaussian Processes in Reduced Eigenbases
Machine Learning, Optimization and Manifolds (MLOMA)

Hassan Maatouk, Didier Rullière, Xavier Bay - Dec. 17, 2023
Large-scale constrained Gaussian processes for shape-restricted function estimation


Benjamin A. Antunes, Claude Mazel, David R.C. Hill - Dec. 9, 2023
Identifying Quality Mersenne Twister Streams For Parallel Stochastic Simulations
Winter Simulation Conference 2023

Anis Fradi, Chafik Samir, José Braga - Dec. 7, 2023
A Shrinkage Method for Learning, Registering and Clustering Shapes of Curves


All publications are here