Maximum likelihood estimation of stochastic chaos representations from experimental data

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A Polynomial Chaos-Based Kalman Filter Approach for Parameter Estimation of Mechanical Systems (2010, methodology): “[54] used a polynomial chaos representation of a random field to be identified, developed a method to estimate the coefficients of that representation, and extended it to apply it to experimental vibration tests using frequency response functions [55].”
The stochastic finite element method: Past, present and future (2009, unknown): “[6,38,39,54,60,73,75,76,84,90,97,114,117,129,152,180,193].”
A generalized spectral decomposition technique to solve a class of linear stochastic partial differential equations (2007, unknown): “The following deﬁ-nition is taken from [17], where it was used to illustrate a method for identiﬁcation of non-Gaussian random ﬁelds.”
Theoretical framework and experimental procedure for modelling mesoscopic volume fraction stochastic fluctuations in fiber reinforced composites (2008, methodology): “The general algorithm is as follows [9]:”
A unified framework for multilevel uncertainty quantification in Bayesian inverse problems (2016, unknown): “Approximate two-stage approaches have been proposed for inferring aleatory parameter variability in inverse problems, e.g. the context of random ﬁelds [73–76].”