> Formation > Cursus ingénieur
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.
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.
Basic knowledge in probability and statistics
The exam is given in english only 
The course exists in the following branches:
))Course ID : WMM9AM23
Course language(s):
You can find this course among all other courses.
Date of update July 11, 2018
Nos sites
e-Services
