S. Consortium, Milestone Deliverable D3: The ATM Target Concept, 2007.

H. Swenson, R. Barhydt, and M. Landis, Next Generation Air Transportation System (NGATS) Air Traffic Management (ATM)-Airspace Project, National Aeronautics and Space Administration, Tech. Rep, 2006.

, Continuous climb operations (cco) manual, 2013.

A. Gerretsen and S. Swierstra, Sensitivity of aircraft performance to variability of input data, EUROCONTROL Doc. CoE-TP-02005, 2003.

F. Huchet, Introduction à la prévision de trajectoire, 2006.

D. Poles, Revision of atmosphere model in bada aircraft performance model, EUROCONTROL, Tech. Rep, 2010.

. Airbus, Getting to grips with the cost index, 1998.

R. Dalmau and X. Prats, Fuel and time savings by flying continuous cruise climbs: Estimating the benefit pools for maximum range operations, Transportation Research Part D: Transport and Environment, vol.35, pp.62-71, 2015.

D. Poles, A. Nuic, and V. Mouillet, Advanced aircraft performance modeling for atm: Analysis of bada model capabilities, Proceedings of the 29th IEEE/AIAA Digital Avionics Systems Conference (DASC), 2010.

W. James, P. O'dell, and R. Royce, Derated climb performance in large civil aircraft, Conference Boeing Performance and Flight Operations Engineering, 2005.

, Ac 25-13 -reduced and derated takeoff thrust (power) procedures, 1988.

M. G. Hamed, R. Alligier, and D. Gianazza, High confidence intervals applied to aircraft altitude prediction, IEEE Transactions on Intelligent Transportation Systems, vol.17, issue.9, pp.2515-2527, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01284915

S. T. Barratt, M. J. Kochenderfer, and S. P. Boyd, Learning probabilistic trajectory models of aircraft in terminal airspace from position data, IEEE Transactions on Intelligent Transportation Systems, 2018.

Y. S. Park and D. P. Thipphavong, Performance of an Adaptive Trajectory Prediction Algorithm for Climbing Aircraft, p.2013

, Aviation Technology, Integration, and Operations Conference. 08, 2013.

R. Alligier, D. Gianazza, M. G. Hamed, and N. Durand, Comparison of Two Ground-based Mass Estimation Methods on Real Data (regular paper), International Conference on Research in Air Transportation (ICRAT), 2014.

M. Uzun and E. Koyuncu, Data-driven trajectory uncertainty quantification for climbing aircraft to improve ground-based trajectory prediction, Anadolu Üniversitesi Bilim Ve Teknoloji Dergisi A-Uygulamal? Bilimler ve Mühendislik, vol.18, pp.323-345, 2017.

J. Sun, J. Ellerbroek, and J. Hoekstra, Bayesian inference of aircraft initial mass, Proceedings of the 12th USA/Europe Air Traffic Management Research and Development Seminar. FAA/EUROCONTROL, 2017.

J. Sun, J. Ellerbroek, and J. M. Hoekstra, Aircraft initial mass estimation using bayesian inference method, Transportation Research Part C: Emerging Technologies, vol.90, pp.59-73, 2018.

J. Sun, H. A. Blom, J. Ellerbroek, and J. M. Hoekstra, Aircraft mass and thrust estimation using recursive bayesian method, 2018.

Y. S. Chati and H. Balakrishnan, Modeling of aircraft takeoff weight using gaussian processes, Journal of Air Transportation, pp.1-10, 2018.

R. Alligier, Predictive Distribution of the Mass and Speed Profile to Improve Aircraft Climb Prediction, ATM Seminar, p.13, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02138151

A. Usa/europe and . Seminar, , 2019.

V. Mouillet, User manual for base of aircraft data (bada) rev.3.14, EUROCONTROL, 2017.

M. Schäfer, M. Strohmeier, V. Lenders, I. Martinovic, and M. Wilhelm, Bringing Up OpenSky: A Large-scale ADS-B Sensor Network for Research, Proceedings of the 13th International Symposium on Information Processing in Sensor Networks, ser. IPSN '14, pp.83-94, 2014.

R. Alligier and D. Gianazza, Learning aircraft operational factors to improve aircraft climb prediction: A large scale multi-airport study, Transportation Research Part C: Emerging Technologies, vol.96, pp.72-95, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01878615

R. Alligier, D. Gianazza, and N. Durand, Machine learning applied to airspeed prediction during climb, Proceedings of the 11th
URL : https://hal.archives-ouvertes.fr/hal-01168664

. Usa/, Europe Air Traffic Management R & D Seminar, 2015.

R. M. Neal, Bayesian learning for neural networks, vol.118, 2012.

J. M. Hernández-lobato and R. Adams, Probabilistic backpropagation for scalable learning of bayesian neural networks, International Conference on Machine Learning, pp.1861-1869, 2015.

A. Graves, Practical variational inference for neural networks, Advances in neural information processing systems, pp.2348-2356, 2011.

S. Kullback, Information theory and statistics. Courier Corporation, 1997.

Y. Gal and Z. Ghahramani, Dropout as a bayesian approximation: Representing model uncertainty in deep learning, international conference on machine learning, pp.1050-1059, 2016.

N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, Dropout: a simple way to prevent neural networks from overfitting, The Journal of Machine Learning Research, vol.15, issue.1, pp.1929-1958, 2014.

C. E. Rasmussen, Gaussian processes in machine learning, Advanced lectures on machine learning, pp.63-71, 2004.

J. Gardner, G. Pleiss, K. Q. Weinberger, D. Bindel, and A. G. Wilson, Gpytorch: Blackbox matrix-matrix gaussian process inference with gpu acceleration, Advances in Neural Information Processing Systems, pp.7576-7586, 2018.

H. Liu, Y. Ong, X. Shen, and J. Cai, When gaussian process meets big data: A review of scalable gps, 2018.

B. Lakshminarayanan, A. Pritzel, and C. Blundell, Simple and scalable predictive uncertainty estimation using deep ensembles, Advances in Neural Information Processing Systems, pp.6402-6413, 2017.

C. M. Bishop, Mixture density networks, 1994.

Y. Ovadia, E. Fertig, J. Ren, Z. Nado, D. Sculley et al., Can you trust your model's uncertainty? evaluating predictive uncertainty under dataset shift, 2019.

M. Hein, M. Andriushchenko, and J. Bitterwolf, Why relu networks yield high-confidence predictions far away from the training data and how to mitigate the problem, The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019.

G. Dorta, S. Vicente, L. Agapito, N. D. Campbell, and I. Simpson, Structured uncertainty prediction networks, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.5477-5485, 2018.

I. Loshchilov and F. Hutter, Decoupled weight decay regularization, International Conference on Learning Representations, 2019.

L. N. Smith, Cyclical learning rates for training neural networks, Applications of Computer Vision (WACV, pp.464-472, 2017.

G. Klambauer, T. Unterthiner, A. Mayr, and S. Hochreiter, Selfnormalizing neural networks, Advances in neural information processing systems, pp.971-980, 2017.