This repository contains the work I did during my third year at ONERA under the supervision of two researchers in the Signal Processing Department. Department. In the context of space engineering, this project was about getting familiar with a sensitivity indicator adapted to multidisciplinary optimisation, the Hilbert-Schmidt Independence Criterion (HSIC). This indicator is based on the Kernel Methods machinery, which makes it all the more interesting to study it in a machine learning context.
An estimator of the HSIC is computed, first for a simple case and then for the more realistic situation of a space satellite. You will also find an implementation of the LAMDA method to estimate the PDF of a random variable using a Monte Carlo method, and the First Order Reliability Method (FORM). Reliability Method (FORM).
OpenTurns, a Python librairy dedicated to the computation of statistical quantities, is heavily utilized here.