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" :
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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.
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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
A K-MEANS, WARD AND DBSCAN REPEATABILITY STUDY
Charlie Sire, Didier Rullière, Rodolphe Le Riche, Jérémy Rohmer, Yann Richet, Lucie Pheulpin - Nov. 28, 2025
Augmented Quantization: Mixture Models for Risk-Oriented Sensitivity Analysis
Soumyodeep Mukhopadhyay, Didier Rullière, Rodolphe Le Riche, Xavier Bay, Laurent Genest, David Gaudrie - Nov. 18, 2025
Optimal Linear Interpolation under Differential Information: application to prediction of perfect flows
PGMO (Programme Gaspard Monge pour l'Optimisation) DAYS 2025
Soumyodeep Mukhopadhyay, Didier Rullière, Rodolphe Le Riche, Laurent Genest, David Gaudrie - Nov. 13, 2025
Optimal Linear Interpolation under Differential Information: application in fluid dynamics
Journées Scientifiques du consortium CIROQUO
Julien Ah-Pine, Nathaniel Gbenro - Nov. 10, 2025
Mixed data k -Anonymization by Consistent Maximal Association and Microaggregation
CIKM '25: The 34th ACM International Conference on Information and Knowledge Management
Didier Rullière, Marc Grossouvre - Oct. 24, 2025
A Joint Kriging Model with Application to Constrained Classification
Statistics and Computing
Didier Rullière, Rodolphe Le Riche, Xavier Bay, Soumyodeep Mukhopadhyay, Victor Trappler, Hassan Maatouk, Tanguy Appriou, Laurent Genest, David Gaudrie, Marc Grossouvre, Christophette Blanchet-Scalliet, Céline Helbert, Sébastien da Veiga, Julien Pelamatti, Reda El Amri, Youssef Diouane, Victor Picheny, Alexandre Scotto Di Perrotolo - Oct. 15, 2025
Gaussian Processes: from knowledge-informed Machine Learning to optimization
Hassan Maatouk, Didier Rullière, Xavier Bay - Sept. 23, 2025
Efficient constrained Gaussian process approximation using elliptical slice sampling
Bayesian Analysis
Zaar Khizar, Johann Laconte, Roland Lenain, Romuald Aufrère - Sept. 2, 2025
Feeling the force: A nuanced physics-based traversability sensor for navigation in unstructured vegetation
European Conference on Mobile Robots
Hassan Maatouk, Didier Rullière, Xavier Bay - Sept. 1, 2025
Bayesian analysis of constrained Gaussian processes
Bayesian Analysis
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
- TRAPPLER Victor
- YON Loïc