, Our contributions are twofold: (i) we have proposed individual models trained on in-air data to improve the aeronautics performance of individual aircrafts, rather than industry-wide calibrated parameters. This allows in particular for the search of more efficient (e.g., flight duration, speed, fuel consumption, etc.) trajectories for aircrafts (ii) we have designed a generic framework combining off-the-shelf machine learning with domain-specific approximations, which can be used in any data-intensive engineering discipline. We certainly hope that this approach can be replicated in other fields of study

J. D. Anderson, Aircraft performance and design. McGraw-Hill international editions: Aerospace science/technology series, 1999.

J. P. Buonaccorsi, Measurement error in the response in the general linear model, Journal of the American Statistical Association, vol.91, issue.434, pp.633-642, 1996.

J. Raymond, D. Carroll, and . Ruppert, Transformation and weighting in regression, vol.30, 1988.

R. Dalmau and X. Prats, How much fuel and time can be saved in a perfect flight trajectory? continuous cruise climbs vs. conventional operations, Proceedings of the 6th International Conference on Research in Air Transportation (ICRAT), 2014.

A. Wayne and . Fuller, Measurement error models, vol.305, 2009.

M. Kaiser, M. Schultz, and H. Fricke, Enhanced jet performance model for high precision 4d flight path prediction, Proceedings of the 1st International Conference on Application and Theory of Automation in Command and Control Systems, p.3340, 2011.

G. Ke, Q. Meng, T. Finley, T. Wang, W. Chen et al., Lightgbm: A highly efficient gradient boosting decision tree, Advances in Neural Information Processing Systems, vol.30, pp.3146-3154, 2017.

L. K. Loftin, Quest for performance: The evolution of modern aircraft. NASA Scientific and Technical Information Branch, 1985.

B. W. Mccormick, Aerodynamics, Aeronautics, and Flights Mechanics, 1995.

A. Nuic, User manual for the base of aircraft data (bada) 3.12, 2014.

A. Nuic, C. Poinsot, M. Iagaru, E. Gallo, F. A. Navarro et al., Advanced aircraft performance modeling for atm: Enhancements to the bada model, 24th Digital Avionics Systems Conference, vol.1, 2005.

F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion et al., Scikit-learn: Machine learning in Python, Journal of Machine Learning Research, vol.12, pp.2825-2830, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00650905

C. , Exploration de donnes pour l'optimisation de trajectoires ariennes, Ecole Polytechnique, 2018.

E. Roux and . Modles-moteurs, Racteurs double flux civils et racteurs militaires faible taux de dilution avec Post-Combustion. INSA-SupAro-ONÉRA, 2002.

E. Roux, Pour une approche analytique de la Dynamique du, 2005.

S. M. Schennach, Recent advances in the measurement error literature, Annual Review of Economics, vol.8, issue.1, pp.341-377, 2016.

J. Sun, J. M. Hoekstra, and J. Ellerbroek, Aircraft drag polar estimation based on a stochastic hierarchical model, Eighth SESAR Innovation Days, 2018.

E. Torenbeek, Synthesis of subsonic airplane design, 1982.