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• Soize, C., Non-Gaussian positive-definite matrix-valued random fields for elliptic stochastic partial differential operators. Comput Methods Appl Mech.Eng. 195 (2006), 26–64, 10.1016/j.cma.2004.12.014.
• Guilleminot, J., Soize, C., On the statistical dependence for the components of random elasticity tensors exhibiting material symmetry properties. J. Elast 111 (2013), 109–130, 10.1007/s10659-012-9396-z.
• Batou, A., Soize, C., Rigid multibody system dynamics with uncertain rigid bodies. Multibody Syst Dyn 27 (2012), 285–319, 10.1007/s11044-011-9279-2.
• Kree, P., Soize, C., Mathematics of random phenomena. 1983, D. Reidel Publishing Company, Dordrecht, Netherlands.
• Roberts, J., Spanos, P., Random vibration and statistical linearization. 2003, Dover, Mineola, NY.
• Beck, J., Katafygiotis, L., Updating models and their uncertainties. I: Bayesian statistical framework. J Eng Mech 124 (1998), 455–461, 10.1061/(ASCE)0733-9399(1998)124:4(455).
• Kennedy, M., O'Hagan, A., Bayesian calibration of computer models. J R Stat SocSeries B 63 (2001), 425–450, 10.1111/1467-9868.00294.
• Arendt, P., Apley, D., Chen, W., Quantification of model uncertainty: calibration, model discrepancy, and identifiability. J Mech Des, 134, 2012, 100908, 10.1115/1.4007390.
• Sargsyan, K., Najm, H., Ghanem, R., On the statistical calibration of physical models. Int J Chem Kinet 47 (2015), 246–276, 10.1002/kin.20906.
• Soize, C., Nonparametric model of random uncertainties for reduced matrix models in structural dynamics. Probab Eng Mech 15 (2000), 277–294, 10.1016/S0266-8920(99)00028-4.
• Capiez-Lernout, E., Soize, C., Nonparametric modeling of random uncertainties for dynamic response of mistuned bladed disks. J Eng Gas TurbinesPower 126 (2004), 610–618, 10.1115/1.1760527.
• Desceliers, C., Soize, C., Cambier, S., Non-parametric-parametric model for random uncertainties in non-linear structural dynamics: application to earthquake engineering. Earthquake Eng StructDyn 33 (2004), 315–327, 10.1002/eqe.352.
• Mignolet, M., Soize, C., Nonparametric stochastic modeling of linear systems with prescribed variance of several natural frequencies. Probab Eng Mech 23 (2008), 267–278, 10.1016/j.probengmech.2007.12.027.
• Soize, C., Capiez-Lernout, E., Durand, J.-F., Fernandez, C., Gagliardini, L., Probabilistic model identification of uncertainties in computational models for dynamical systems and experimental validation. Comput Methods Appl MechEng 198 (2008), 150–163, 10.1016/j.cma.2008.04.007.
• Soize, C., Poloskov, I., Time-domain formulation in computational dynamics for linear viscoelastic media with model uncertainties and stochastic excitation. Comput Math Appl 64 (2012), 3594–3612, 10.1016/j.camwa.2012.09.010.
• Durand, J.-F., Soize, C., Gagliardini, L., Structural-acoustic modeling of automotive vehicles in presence of uncertainties and experimental identification and validation. J Acoust SocAm 124 (2008), 1513–1525, 10.1121/1.2953316.
• Soize, C., Chebli, H., Random uncertainties model in dynamic substructuring using a nonparametric probabilistic model. J Eng Mech 129 (2003), 449–457, 10.1061/(ASCE)0733-9399(2003)129:4(449).
• Chebli, H., Soize, C., Experimental validation of a nonparametric probabilistic model of nonhomogeneous uncertainties for dynamical systems. J Acoust SocAm 115 (2004), 697–705, 10.1121/1.1639335.
• Arnst, M., Clouteau, D., Cheli, H., Othman, R., Degrande, G., A non-parametric probabilistic model for ground-borne vibrations in buildings. Probab Eng Mech 21 (2006), 18–34, 10.1016/j.probengmech.2005.06.004.
• Cottereau, R., Clouteau, D., Soize, C., Construction of a probabilistic model for impedance matrices. Comput Methods Appl MechEng 196 (2007), 2252–2268, 10.1016/j.cma.2006.12.001.
• Cottereau, R., Clouteau, D., Soize, C., Parametric and nonparametric models of the impedance matrix of a random medium. Eur J ComputMech 17 (2008), 881–892, 10.3166/REMN.17.881-892.
• Arnst, M., Clouteau, D., Bonnet, M., Inversion of probabilistic structural models using measured transfer functions. Comput Methods Appl MechEng 197 (2008), 589–608, 10.1016/j.cma.2007.08.011.
• Soize, C., Generalized probabilistic approach of uncertainties in computational dynamics using random matrices and polynomial chaos decompositions. Int J Numer Methods Eng 81 (2010), 939–970, 10.1002/nme.2712.
• Batou, A., Soize, C., Stochastic modeling and identification of an uncertain computational dynamical model with random fields properties and model uncertainties. Arch Appl Mech 83 (2013), 831–848, 10.1007/s00419-012-0720-7.
• Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., et al. Global sensitivity analysis: the primer. 2008, Wiley, West Sussex, United Kingdom.
• Sobol, I., Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates. Math Comput Simul 55 (2001), 271–280, 10.1016/S0378-4754(00)00270-6.
• Saltelli, A., Making best use of model evaluations to compute sensitivity indices. Comput Phys Commun 145 (2002), 280–297, 10.1016/S0010-4655(02)00280-1.
• Oakley, J., O'Hagan, A., Probabilistic sensitivity analysis of complex models: a Bayesian approach. J R Stat SocSeries B 66 (2004), 751–769, 10.1111/j.1467-9868.2004.05304.x.
• Sudret, B., Global sensitivity analysis using polynomial chaos expansions. Reliab Eng Syst Safety 93 (2008), 964–979, 10.1016/j.ress.2007.04.002.
• Crestaux, T., Le Maître, O., Martinez, J.-M., Polynomial chaos expansion for sensitivity analysis. Reliab Eng Syst Safety 94 (2009), 1161–1172, 10.1016/j.ress.2008.10.008.
• Kucherenko, S., Tarantola, S., Annoni, P., Estimation of global sensitivity indices for models with dependent variables. Comput Phys Commun 183 (2012), 937–946, 10.1016/j.cpc.2011.12.020.
• Janon, A., Klein, T., Lagnoux, A., Nodet, M., Prieur, C., Asymptotic normality and efficiency of two sobol index estimators. ESAIM - Probab Stat 18 (2014), 342–364, 10.1051/ps/2013040.
• Owen, A., Better estimation of small sobol’ sensitivity indices. ACM Trans Model ComputSimul, 23, 2013, 11, 10.1145/2457459.2457460.
• Li, G., Wang, S., Rabitz, H., Practical approaches to construct RS-HDMR component functions. J Phys Chem A 106 (2002), 8721–8733, 10.1021/jp014567t.
• Munoz Zuniga, M., Kucherenko, S., Shah, N., Metamodelling with independent and dependent inputs. Comput Phys Commun 184 (2013), 1570–1580, 10.1016/j.cpc.2013.02.00.
• Kucherenko, S., Song, S., Different numerical estimators for main effect global sensitivity indices. Reliab Eng Syst Safety 165 (2017), 222–238, 10.1016/j.ress.2017.04.003.
• Craig, R., Bampton, M., Coupling of substructures for dynamic analysis. AIAA J 6 (1968), 1313–1319, 10.2514/3.4741.