Skip to Main content Skip to Navigation
Theses

Machine Learning for the prediction of aeronautical loads and stress.

Abstract : This thesis focuses on Machine Learning and information extraction for aeronautical loads and stress data. In the first time, we carry out a study for the prediction of aeronautical loads curves. We compare regression trees based models and quantify the influence of dimension reduction techniques on regression performances in an extrapolation context. In the second time, we develop a deformation model acting simultaneously on the input and the output space of the curves. We study the asymptotic properties of the estimators of the deformation parameters. This deformation model is associated to the modeling and predicting process of aeronautical loads. Finally, we give a simple and efficient method for predicting critical loads.
Document type :
Theses
Complete list of metadatas

Cited literature [55 references]  Display  Hide  Download

https://tel.archives-ouvertes.fr/tel-02555665
Contributor : Laurence Porte <>
Submitted on : Monday, April 27, 2020 - 2:47:31 PM
Last modification on : Tuesday, October 20, 2020 - 10:32:07 AM

File

2019TOU30123.pdf
Files produced by the author(s)

Licence


Distributed under a Creative Commons Attribution - NonCommercial - ShareAlike 4.0 International License

Identifiers

  • HAL Id : tel-02555665, version 1

Collections

Citation

Edouard Fournier. Machine Learning for the prediction of aeronautical loads and stress.. Mathematics [math]. Université Toulouse 3, 2019. English. ⟨tel-02555665⟩

Share

Metrics

Record views

128

Files downloads

61