M. X. Li, E. G. Berggren, M. Berg, and I. Persson, Assessing track geometry quality based on wavelength spectra and track???vehicle dynamic interaction, Vehicle System Dynamics, vol.46, issue.sup1, pp.261-276, 2008.
DOI : 10.1076/vesd.34.4.261.2060

G. Lonnbark, Characterization of track irregularities with respect to vehicle response, Royal Institute of Technology, 2012.

G. Perrin, C. Soize, D. Duhamel, and C. Funfschilling, Track irregularities stochastic modeling, Probabilistic Engineering Mechanics, vol.34, pp.123-130, 2013.
DOI : 10.1016/j.probengmech.2013.08.006

URL : https://hal.archives-ouvertes.fr/hal-00850645

S. Kraft, Parameter identification for a TGV model, Thèse de Doctorat, 2012.
URL : https://hal.archives-ouvertes.fr/tel-00731143

G. Perrin, Random fields and associated statistical inverse problems for uncertainty quantifications -Applications to railway track geometries for high-speed trains dynamical responses and risk assessment, Thèse de Doctorat, 2013.

G. Perrin, C. Soize, D. Duhamel, and C. Funfschilling, Identification of Polynomial Chaos Representations in High Dimension from a Set of Realizations, SIAM Journal on Scientific Computing, vol.34, issue.6, pp.2917-2945, 2012.
DOI : 10.1137/11084950X

URL : https://hal.archives-ouvertes.fr/hal-00770006

C. Soize, Identification of high-dimension polynomial chaos expansions with random coefficients for non-Gaussian tensor-valued random fields using partial and limited experimental data, Computer Methods in Applied Mechanics and Engineering, vol.199, issue.33-36, pp.2150-2164, 2010.
DOI : 10.1016/j.cma.2010.03.013

URL : https://hal.archives-ouvertes.fr/hal-00684324

G. Perrin, C. Soize, D. Duhamel, and C. Funfschilling, Karhunen???Lo??ve expansion revisited for vector-valued random fields: Scaling, errors and optimal basis., Journal of Computational Physics, vol.242, issue.1, pp.607-622, 2013.
DOI : 10.1016/j.jcp.2013.02.036

G. Perrin, C. Soize, D. Duhamel, and C. Funfschilling, A Posteriori Error and Optimal Reduced Basis for Stochastic Processes Defined by a Finite Set of Realizations, SIAM/ASA Journal on Uncertainty Quantification, vol.2, issue.1, 2013.
DOI : 10.1137/130905095

URL : https://hal.archives-ouvertes.fr/hal-01097139

R. Ghanem and P. Spanos, Stochastic finite elements: a spectral approach, 1991.
DOI : 10.1007/978-1-4612-3094-6

O. Le-maitre and O. Knio, Spectral Methods for Uncertainty Quantification, 2010.
DOI : 10.1007/978-90-481-3520-2

C. Soize, Stochastic Models of Uncertainties in Computational Mechanics, ASCE
DOI : 10.1061/9780784412237

URL : https://hal.archives-ouvertes.fr/hal-00749201

G. R. Terrell and D. W. Scott, Variable Kernel Density Estimation, The Annals of Statistics, vol.20, issue.3, pp.1236-1265, 1992.
DOI : 10.1214/aos/1176348768

A. W. Bowman and W. Azzalini, Applied Smoothing Techniques for Data Analysis, 1997.