Model the uncertainties

A product can have variable performance and behaviour during its lifespan, depending on the stresses it is subjected to or its characteristics. The lack of knowledge and the imprecision of experimental measurements motivate the modelling of product-related uncertainties. The MS course introduces the fundamental elements for modelling uncertainties and analysing product performance in all cases.

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2-day course

Objectives

  • Understand the reasons behind the probabilistic approach
  • Learn how to build a probabilistic model
  • Be able to critically examine the results of a statistical analysis

 

Prerequisites

  • Basic knowledge of mathematics.
  • Good knowledge of Python.

AUDIENCE

  • R&D Engineer
  • Design Office Engineer
  • Operational Safety Engineer

 

Upcoming sessions

  • March 18-19, 2021
  • April 8-9, 2021
  • September 16-17, 2021
  • November 25-26, 2021

PLACE

18/20 Boulevard Reuilly, 75012 PARIS – Métro Dugommier


MANAGER

Sylvain GIRARD

Program

Why use randomness in modelling?

Fundamental concepts of statistics and probability

  • Random variable and probability law
  • Uniform and Gaussian Laws
  • Expectancy and variance
  • Distribution function and density

Couple of random variables, conditioning and regression

  • Interactions between random variables
  • Conditional probabilities
  • Predicting with data: the linear model

Build a model and decide on a sample

  • Modelling uncertainty using random variables
  • Inferring parameters using a sample
  • Quantitatively testing assumptions and making decisions

Application exercises (paper – pencil or Python)

catalogue_2021_incertitude

Application form

Register or ask for further information. Personalised training courses can also be provided. The location of the training is subject to change. Training courses can be held at a distance. In this case, the training can be delivered in several short sessions (e.g. 2 hours) via videoconference software, spread over several days, in agreement with the trainer and the participants.



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