Aircraft Numerical “Twin”: A Time Series Regression Competition

Adrien Pavão,Isabelle Guyon, Nachar Stéphane, Fabrice Lebeau,Martin Ghienne,Ludovic Platon,Tristan Barbagelata, Pierre Escamilla,Sana Mzali, Meng Liao,Sylvain Lassonde,Antonin Braun,Slim Ben Amor,Liliana Cucu-Grosjean, Marwan Wehaiba,Avner Bar-Hen,Adriana Gogonel,Alaeddine Ben Cheikh,Marc Duda, Julien Laugel, Mathieu Marauri,Mhamed Souissi, Théo Lecerf,Mehdi Elion,Sonia Tabtill,Julien Budynek,Pauline Le Bouteiller, Antonin Penon, Raphaël-David Lasseri, Julien Ripoche, Thomas Epalle

2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA)（2021）

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摘要

This paper presents the design and analysis of a data science competition on a problem of time series regression from aeronautics data. For the purpose of performing predictive maintenance, aviation companies seek to create aircraft “numerical twins”, which are programs capable of accurately predicting strains at strategic positions in various body parts of the aircraft. Given a number of input pa...

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关键词

Measurement,Deep learning,Codes,Atmospheric modeling,Time series analysis,Numerical models,Aircraft

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