M. Anderson and R. M. May, Infectious Diseases of Humans: Dynamics and Control, 1992.

A. Argyriou, T. Evgeniou, and M. Pontil, Convex multi-task feature learning, Machine Learning, 2008.

F. Bach, Bolasso: model consistent Lasso estimation through the bootstrap, Proceedings of the 25th International Conference on Machine Learning, pp.33-40, 2008.
URL : https://hal.archives-ouvertes.fr/hal-00271289

J. T. Betts, Practical Methods for Optimal Control and Estimation Using Nonlinear Programming, 2010.

J. F. Bonnans, D. Giorgi, V. Grelard, B. Heymann, S. Maindrault et al., Bocop-A collection of examples, INRIA, 2017.
URL : https://hal.archives-ouvertes.fr/hal-00726992

R. Caruana, Multitask learning, Machine Learning, vol.28, pp.41-75, 1997.

J. De-leeuw, F. W. Young, and Y. Takane, Additive structure in qualitative data: An alternating least squares method with optimal scaling features, Psychometrika, vol.41, issue.4, pp.471-503, 1976.

S. Dean, H. Mania, N. Matni, B. Recht, and S. Tu, On the sample complexity of the linear quadratic regulator, 2017.

B. Efron, T. Hastie, I. Johnstone, and R. Tibshirani, Least angle regression, The Annals of Statistics, vol.32, pp.407-499, 2004.

T. Evgeniou, C. A. Micchelli, and M. Pontil, Learning multiple tasks with kernel methods, Journal Machine Learning Research, vol.6, pp.615-637, 2005.

R. Fitzhugh, Impulses and physiological states in theoretical models of nerve membrane, Biophysical journal, vol.1, issue.6, pp.445-466, 1961.

J. Friedman, T. Hastie, and R. Tibshirani, , 2010.

A. E. Hoerl, Application of ridge analysis to regression problems, Chemical Engineering Progress, vol.58, pp.54-59, 1962.

D. G. Hull, Fundamentals of Airplane Flight Mechanics, 2007.

R. V. Jategaonkar, Flight Vehicle System Identification: A Time Domain Methdology, 2006.

V. Klein and E. A. Morelli, Aircraft System Identification, 2006.

B. Krishnapuram, L. Carin, M. A. Figueiredo, and A. J. Hartemink, Sparse multinomial logistic regression: Fast algorithms and generalization bounds, IEEE Transactions on Pattern Analysis & Machine Intelligence, issue.6, pp.957-968, 2005.

K. Levenberg, A method for the solution of certain non-linear problems in least squares, Quarterly of applied mathematics, vol.2, issue.2, pp.164-168, 1944.

L. Ljung, System Identification: Theory for the User, 1987.

L. Ljung, Perspectives on system identification, Annual Reviews in Control, vol.34, issue.1, pp.1-12, 2010.

L. Ljung and S. T. Glad, On global identifiability for arbitrary model parameterizations, Automatica, vol.30, issue.2, pp.265-276, 1994.

J. Lokhorst, The lasso and generalised linear models. Honors Project, The University of Adelaide, 1999.

R. E. Maine and K. W. Iliff, Identification of Dynamic Systems: Theory and Formulation. NASA, STIB, 1985.

D. W. Marquardt, An algorithm for least-squares estimation of nonlinear parameters, Journal of the society for Industrial and Applied Mathematics, vol.11, issue.2, pp.431-441, 1963.

N. Meinshausen and P. Bühlmann, Stability selection, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.72, issue.4, pp.417-473, 2010.

J. Nagumo, S. Arimoto, and S. Yoshizawa, An active pulse transmission line simulating nerve axon, Proceedings of the IRE, vol.50, issue.10, pp.2061-2070, 1962.

G. Obozinski, B. Taskar, and M. I. Jordan, Multi-task feature selection, ICML-06 Workshop on Structural Knowledge Transfer for Machine Learning, 2006.

F. Pedregosa, 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

N. K. Peyada and A. K. Ghosh, Aircraft parameter estimation using a new filtering technique based upon a neural network and Gauss-Newton method, The Aeronautical Journal, vol.113, pp.243-252, 1142.

N. K. Peyada, A. Sen, and A. K. Ghosh, Aerodynamic characterization of hansa-3 aircraft using equation error, maximum likelihood and filter error methods, Proceedings of the International MultiConference of Engineers and Computer Scientists, 2008.

J. O. Ramsay, G. Hooker, D. Campbell, and J. Cao, Parameter estimation for differential equations: a generalized smoothing approach, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.69, issue.5, pp.741-796, 2007.

B. Recht, A tour of reinforcement learning: The view from continuous control, 2018.

J. F. Ritt, Differential Algebra, vol.33, 1950.

C. Rommel, J. F. Bonnans, B. Gregorutti, and P. Martinon, Aircraft dynamics identification for optimal control, Proceedings of the 7th European Conference for Aeronautics and Aerospace Sciences, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01639731

D. Silver, J. Schrittwieser, K. Simonyan, I. Antonoglou, A. Huang et al., Mastering the game of Go without human knowledge, Nature, vol.550, issue.7676, p.354, 2017.

R. Tibshirani, Regression shrinkage and selection via the Lasso, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.58, pp.267-288, 1994.

R. Tibshirani, The lasso method for variable selection in the Cox model. Statistics in medicine, vol.16, pp.385-395, 1997.

A. N. Tikhonov, On the stability of inverse problems, Doklady Akademii Nauk SSSR, vol.39, pp.195-198, 1943.

S. Van-de-geer, 1-regularization in high-dimensional statistical models, Proceedings of the International Congress of Mathematicians 2010 (ICM 2010), vol.4, pp.2351-2369, 2010.

J. M. Varah, A spline least squares method for numerical parameter estimation in differential equations, SIAM Journal on Scientific and Statistical Computing, vol.3, issue.1, pp.28-46, 1982.

A. Wächter and L. T. Biegler, On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical programming, vol.106, pp.25-57, 2006.

M. Yuan and Y. Lin, Model selection and estimation in regression with grouped variables, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.68, pp.49-67, 2005.

P. Zhao and B. Yu, On model selection consistency of Lasso, Journal of Machine Learning Research, vol.7, pp.2541-2563, 2006.

H. Zou, The adaptive Lasso and its oracle properties, Journal of the American Statistical Association, vol.101, pp.1418-1429, 2006.

H. Zou and T. Hastie, Regularization and variable selection via the elastic net, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.67, issue.2, pp.301-320, 2005.