Aircraft Numerical "Twin": A Time Series Regression Competition

International audienceThis 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 parameters (sensor data) recorded in sequence during the flight, the competition participants had to predict output values (gauges), also recorded sequentially during test flights, but not recorded during regular flights. The competition data included hundreds of complete flights. It was a code submission competition with complete blind testing of algorithms. The results indicate that such a problem can be effectively solved with gradient boosted trees, after preprocessing and feature engineering. Deep learning methods did not prove as efficient

Taming Nuclear Complexity with a Committee of Multilayer Neural Networks

International audienceWe demonstrate that a committee of deep neural networks is capable of predicting the ground-state and excited energies of more than 1800 atomic nuclei with an accuracy akin to the one achieved by state-of-the-art nuclear energy density functionals (EDFs) and with significantly less computational cost. An active learning strategy is proposed to train this algorithm with a minimal set of 210 nuclei. This approach enables future fast studies of the influence of EDF parametrizations on structure properties over the whole nuclear chart and suggests that for the first time a machine learning framework successfully encoded several correlated aspects of nuclear deformation

Fouille de données hétérogènes pour la caractérisation de fake news : analyse empirique croisée de la presse en ligne et Twitter

International audienc

Fouille de données hétérogènes pour la caractérisation de rumeurs et fake news : analyse empirique croisée de la presse en ligne et Twitter

International audienc

Aircraft Numerical "Twin": A Time Series Regression Competition

International audienceThis 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 parameters (sensor data) recorded in sequence during the flight, the competition participants had to predict output values (gauges), also recorded sequentially during test flights, but not recorded during regular flights. The competition data included hundreds of complete flights. It was a code submission competition with complete blind testing of algorithms. The results indicate that such a problem can be effectively solved with gradient boosted trees, after preprocessing and feature engineering. Deep learning methods did not prove as efficient