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
Truncated multivariate normal distribution under linear and nonlinear constraints
Technometrics
Razak Christophe Sabi Gninkou, Andrés F. López-Lopera, Franck Massa, Rodolphe Le Riche - March 6, 2026
Scalable multitask Gaussian processes for complex mechanical systems with functional covariates
Rodolphe Le Riche - Feb. 9, 2026
Including domain knowledge in Bayesian Optimization
Dagstuhl Seminar 26072: Best Practice for Leveraging Domain Knowledge in Real-World Optimization
David R.C. Hill, Benjamin A. Antunes - Jan. 23, 2026
Applications and limits of the Grover Quantum Algorithm
Rui Ma, Renaud Chicoisne, Pierre Latouche - Jan. 20, 2026
On partial convexification cuts for mixed-integer quadratic programs with 0-1 indicators
Anthony Bertrand, Engelbert Mephu Nguifo, Violaine Antoine, David Hill - Dec. 19, 2025
A K-Means, Ward and DBSCAN Repeatability Study
Noé Lebreton, Julien Ah-Pine, Julien Jacques, Matthieu Neveu - Dec. 4, 2025
Pattern matching for multivariate time series forecasting
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
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