A. Alarcon, C. Bodel, and M. Bonnet, A coupled unscented Kalman filter and modified error in constitutive relation technique for structural dynamics identification, Proc. EURODYN, pp.2163-2168, 2011.

O. Allix, P. Feissel, and H. Nguyen, Identification strategy in the presence of corrupted measurements, Engineering Computations, pp.487-504, 2005.

F. Chinesta, R. Keunings, and A. Leygue, The Proper Generalized Decomposition for advanced Numerical Simulations, 2014.
DOI : 10.1007/978-3-319-02865-1

F. Darema, Dynamic Data Driven Applications Systems: A New Paradigm for Application Simulations and Measurements, 4th International Conference, pp.662-669, 2004.
DOI : 10.1007/978-3-540-24688-6_86

S. Julier and J. Uhlmann, A new extension of the kalman filter to nonlinear systems, The 11th Int, Symposium on Aerospace/Defense Sensing and Simulation and Controls, 1996.

R. Kalman, A New Approach to Linear Filtering and Prediction Problems, Journal of Basic Engineering, vol.82, issue.1, pp.35-45, 1960.
DOI : 10.1115/1.3662552

P. Ladevèzeladev-`-ladevèze, D. Nedjar, and M. Reynier, Updating of finite element models using vibration tests, AIAA Journal, vol.32, issue.7, pp.1485-1491, 1994.
DOI : 10.2514/3.12219

E. Prudencio, P. Bauman, D. Faghihi, K. Ravi-chandar, and J. Oden, A computational framework for dynamic data-driven material damage control, based on Bayesian inference and model selection, International Journal for Numerical Methods in Engineering, vol.22, issue.3, 2014.
DOI : 10.1002/nme.4669