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From 3 May 2016 to 4 May 2016

Uncertainty quantification: theory and application to algorithms, CFD and geosciences

nasri |  

Announced
Deadline for registration: 15 days before the starting date of each training
Duration : 2 days / (14 hours)

Pre-registration

 

Abstract

This training course is given by three specialists of uncertainty quantification: Dr Pietro Marco Congedo – researcher at INRIA Bordeaux Sud-Ouest, Dr Vivien Mallet – researcher at INRIA Paris Rocquencourt, and Dr Pierre Sagaut – professor at Laboratory M2P2, Aix-Marseille University. The first half of the course focuses on theoretical aspects: uncertainties definition, Monte Carlo methods, projection methods, approximation methods and calibration data methods. The second half of the course is dedicated to application of uncertainty quantification to the fields of algorithm, CFD and geosciences.

Target participants

This training session is for engineers, physicists, computer scientists
and numerical analysts who wish to learn about uncertainty quantification.

Prerequisites

None.

Scientific contacts :

– Dr Pietro Marco Congedo, INRIA Bordeaux Sud-Ouest
– Dr Vivien Mallet, INRIA Paris Rocquencourt
– Pr Pierre Sagaut, Laboratory M2P2 – Aix-Marseille University

Fee

  • Trainees/PhDs/PostDocs : 100 €
  • CERFACS shareholders/CNRS/INRIA : 300 €
  • Public : 600 €

Program

(Everyday from 9h00 to 17h30)

Day1: Theoretical aspects and applications (Pr P. Sagaut)

Introduction and definition: errors, uncertainty, stochastic modelling, sources of uncertainty
Projection methods: Galerkin methods and collocation, generalized polynomial chaos, implementation, etc. Examples in the field of CFD
Approximant methods: kriging, co-kriging, prediction error, POD.
Examples in the field of CFD

Day 2 Morning: Application to algorithms (Dr P.M. Congedo)

Innovative algorithm for quantifying uncertainties in CFD applications
Non-probabilistic approach and epistemic uncertainty
Optimization under uncertainties

Day 2 Afternoon: Application to geosciences, Ensemble evaluation, Data assimilation (Dr V. Mallet)

The multiple models
Ensemble evaluation
A posteriori uncertainty estimate for data assimilation

 

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