CQFD Research team
Quality control and dynamic reliability
The core component of our scientific agenda focuses on the development of statistical and probabilistic methods for the modeling and the optimization of complex systems. These systems require mathematical representations which are in essence dynamic and stochastic with discrete and/or continuous variables. This increasing complexity poses genuine scientific challenges that can be addressed through complementary approaches and methodologies:
- Modeling: design and analysis of realistic and tractable models for such complex real-life systems taking into account various probabilistic phenomena;
- Estimation and evaluation: developing theoretical and computational methods in order to estimate the parameters of the model and to evaluate the performance of the system;
- Optimal Control: developing theoretical and numerical control tools to optimize the performance.
- For the statistical part, we are especially interested in dimension reduction approaches in semi-parametric modeling and exploratory data analysis. The common aim is to estimate lower dimensional subspaces minimizing the loss of some statistical information.
- The control part is focused on theoretical and numerical aspects of stochastic optimal control of two general classes of stochastic processes: piecewise deterministic Markov processes and Markov decision processes.
International and industrial relations
International collaborations :
Eduardo Fontura Costa (Universidade de Sao Paulo, Brazil); Oswaldo Luiz do Valle Costa (Escola Politecnica da Universidade de Sao Paulo, Brazil); Benoit Liquet (The University of Queensland, Australia); Davy Paindaveine (Université Libre de Bruxelles); Alexei Piunovskiy (University of Liverpool, United Kingdom); Tomas Prieto-Rumeau (UNED, Madrid, Spain); Yi Zhang (University of Liverpool, United Kingdom).
Industrial collaborations :
Astrium Space Transportation; Airbus Defence & Space; DCNS; EDF; LyRe; Thales Optronique
Keywords: Markov chain; Piecewise Deterministic Markov Processes Markov Decision Processes; Dimension Reduction Models; Stochastic control; semi parametric and non parametric modeling; multivariate data analysis.