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Digital tools and methods - 2021

Estimation and optimization of the performance of complex systems

(Stochastic Algorithms)


This course is intended for general engineers and will provide:

  • An understanding of the principles of stochastic algorithm methods (eg Monte Carlo, particulate, etc.)
  • A choice of methods best suited to the problem of estimation or optimization being considered
  • Techniques to put into practice for realistic applications


The course lasts 4 days (24 hours) and includes:

  • 12 hours of theory-based learning with applications and examples
  • 12 hours of PC-based application enabling learners to put into practice the methods taught for aerospace test cases


The usual estimation or optimization techniques are very quickly surpassed for complex and/or large-scale engineering problems such as the design of space launchers, the estimation of aircraft reliability or the optimization of the positioning of sensor networks.

Stochastic Monte-Carlo algorithms, inspired by games of chance, offer an efficient alternative for estimating and optimizing random parameters or calculating integrals efficiently. The development of these algorithms is currently fast-growing and many variants are available.

This course therefore offers an algorithmic and applicative overview of stochastic techniques and particle simulation methods. A day of training includes a morning course to present the different algorithms and an afternoon of practical PC-based work to implement and test these techniques on concrete problems from the aeronautics and space sectors.


Course level: Basic/Advanced

Engineering-level or equivalent training required.

Statistical knowledge is reviewed quickly at the beginning of the course.

Basic knowledge of Matlab and Python is required.


Jérôme MORIO:

Senior Research Fellow at ONERA (Department of Information Processing and Systems).


(In connection with aeronautical and space applications)

  • Summary of probability and statistics
  • Classic Monte Carlo method
  • Variance reduction methods
  • Bootstrap technique
  • Summary of Markov chains
  • Markov Chain Monte Carlo Methods (MCMC)
  • Multi-level simulation (subset simulation)
  • Sequential Monte Carlo methods
  • Particulate filtering (Kalman, SIS, SISR, etc.)
  • Summary of optimization
  • Simulated annealing, genetic algorithms, particle swarm optimization
  • Probability law restart algorithms (CMA-ES, cross entropy)
  • Stress management, multi-objective optimization


Scheduled in French:

TOULOUSE: 27 to 30 September 2021


For the English realization, please, consult us.


€2,200 excluding tax (20% VAT)

See general terms


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