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Informatique et Mathématiques appliquées
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> Formation > Cursus ingénieur

Model exploration for approximation of complex, high-dimensional problems - WMM9AM23

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  • Number of hours

    • Lectures : 18.0
    • Tutorials : -
    • Laboratory works : -
    • Projects : -
    • Internship : -
    • Written tests : -
    ECTS : 3.0
  • Officials : Clementine PRIEUR, Olivier ZAHM

Goals

The goal of this lecture is to address the difficult problem of approximating high-dimensional functions, meaning functions of a large number of parameters. The first part of the lecture is devoted to interpolation techniques via polynomial functions or via gaussian processes. In the second part, we present two methods for reducing the dimension of the input parameters space, namely the Sliced Inverse Regression and the Ridge Function Recovery.

Content

Many industrial applications invole expensive computational codes which can take weeks or months to run. It is typical for weather prediction, in aerospace sector or in the civil engineering field. There is here an important (economic) challenge to reduce the computational cost by constructing a surrogate for the input-to-output relationship.
Since only a few number of model runs is affordable, dedicated tools have been developed to exploit this type of "not-so-big" data sets. This lecture focuses on some of the most recent advances in that direction.

Prerequisites

Basic knowledge in probability and statistics

Tests

The exam is given in english only FR

Calendar

The course exists in the following branches:

  • Curriculum - Master 2 in Applied Mathematics - Semester 9 (this course is given in english only EN)
  • Curriculum - Master 2 in Applied Mathematics - Semester 9 (this course is given in english only EN)
see the course schedule for 2020-2021

Additional Information

Course ID : WMM9AM23
Course language(s): FR

You can find this course among all other courses.

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Date of update July 11, 2018

Université Grenoble Alpes