  In aircraft industry, market needs evolve quickly in a high competitiveness
context. This requires adapting a given aircraft model in minimum time
considering for example an increase of range or the number of passengers (cf
A330 NEO family). The computation of loads and stress to resize the airframe is
on the critical path of this aircraft variant definition: this is a consuming
and costly process, one of the reason being the high dimen-sionality and the
large amount of data. This is why Airbus has invested since a couple of years
in Big Data approaches (statistic methods up to machine learning) to improve
the speed, the data value extraction and the responsiveness of this process.
This paper presents recent advances in this work made in cooperation between
Airbus, ENAC and Institut de Math{\'e}-matiques de Toulouse in the framework of
a proof of value sprint project. It compares the influence of three dimensional
reduction techniques (PCA, polynomial fitting, combined) on the extrapolation
capabilities of Regression Trees based algorithms for loads prediction. It
shows that AdaBoost with Random Forest offers promising results in average in
terms of accuracy and computational time to estimate loads on which a PCA is
applied only on the outputs.
