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Experiment design, reliability, uncertainty


  • Linear covariance models, multiple interactions, mixed models.
  • Principle of randomized experiments and classical experiments design
  • Factorial, fractional designs
  • Examples with the SAS or JMP software 
  • Numerical design of experiments (quasi-Monte Carlo sequences, Latin Hypersquares)
  • Response surfaces and metamodels (polynomials, kriging, links to statistical learning).
  • Analysis of uncertainties for numerical simulations: propagation of uncertainties, reliability methods (FORM/SORM, order statistics ..) , sensitivity analysis
  • Industrial applications: design with uncertainties, risk analysis.


At the end of this module, the student will have understood and be able to explain:

  • the main methods of experimental design,
  • the analysis of uncertainties,
  • the reliability methods,
  • the probability estimation of  of rare events.


The student will be able to:

  • plan an experiment in the framework of a linear model,
  • built a response surface
  • perform a sensitivity analysis for the treatment of uncertainties in numerical simulation.

Needed prerequisite

Elements of statistical modelling [I4MMMS71]
Softwares and methods of statistical exploratory data analysis [4MMSP81]

Form of assessment

The evaluation of outcome prior learning is made as a continuous training during the semester. According ot the teaching, the assessment will be different: as a written exam, an oral exam, a record, a written report, peers review...