Next Trainings

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).