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
News
last publications
Rank-based Linear-Quadratic Surrogate Assisted CMA-ES
Armel Soubeiga, Violaine Antoine, Sylvain Moreno - May 9, 2025
Comparative analysis of multidimensional sequential trajectories clustering methods
Anis Fradi, Tien-Tam Tran, Chafik Samir - April 7, 2025
Decomposed Gaussian Processes for Efficient Regression Models with Low Complexity
Entropy
Didier Rullière, Marc Grossouvre - April 2, 2025
Some considerations on Kriging, Constraints and Classification
Journées de statistique et optimisation en Occitanie (JS2O)
Achref Ouni, Chafik Samir, Yousef Bouaziz, Anis Fradi - Feb. 26, 2025
ConvKAN: Towards Robust, High-Performance and Interpretable Image Classification
20th International Conference on Computer Vision Theory and Applications
Hassan Maatouk, Didier Rullière, Xavier Bay - Feb. 17, 2025
Efficient constrained Gaussian process approximation using elliptical slice sampling
Anthony Quintin, Tom Petit, Rudy Chocat, Cécile Mattrand, Jean-Marc Bourinet - Feb. 1, 2025
Uncertainty quantification of the reference temperature T0 of 16MND5 steel from experimental and numerical fracture toughness tests
Engineering Fracture Mechanics
Renaud Chicoisne, Pierre Latouche, Rui Ma - Jan. 31, 2025
On Strenghtenings for the Feature Selection Problem
Armel Soubeiga, Thomas Guyet, Violaine Antoine - Jan. 29, 2025
Soft-ECM : une extension de l'algorithme Evidentiel C-Means pour des données complexes
Extraction et Gestion des Connaissances, EGC'2025
Hassan Maatouk, Didier Rullière, Xavier Bay - Jan. 16, 2025
Bayesian linear models for large datasets: Markov chain Monte Carlo or Matheron's update rule
All publications are here
Team
Persons in charge
- KOKO Jonas - Mail
- HILL David - Mail
Lecturing Researchers
- AH-PINE Julien
- BACHELET Bruno
- BAY Xavier
- BOURINET Jean Marc
- CHICOISNE Renaud
- HILL David
- KOKO Jonas
- MAZEL Claude
- RULLIERE Didier
- SAMIR Chafik
- YON Loïc