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Maximum likelihood estimation of stochastic chaos representations from experimental data
2 references·16 citing·31 second hop
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References

  • 1991Stochastic Finite Elements: A Spectral Approach5,753 cited
  • 2004Physical Systems with Random Uncertainties: Chaos Representations with Arbitrary Probability Measure609 cited

Cited by

  • 2012Stochastic Models of Uncertainties in Computational Mechanics112 cited
  • 2015A unified framework for multilevel uncertainty quantification in Bayesian inverse problems103 cited
  • 2010Identification of Bayesian posteriors for coefficients of chaos expansions103 cited
  • 2008Stochastic finite elements: Computational approaches to stochastic partial differential equations98 cited
  • 2012Uncertainty quantification in computational stochastic multiscale analysis of nonlinear elastic materials98 cited
  • 2007Modeling diffusion in random heterogeneous media: Data-driven models, stochastic collocation and the variational multiscale method96 cited
  • 2009Generalized probabilistic approach of uncertainties in computational dynamics using random matrices and polynomial chaos decompositions93 cited
  • 2011Kernel principal component analysis for stochastic input model generation93 cited
  • 2008A scalable framework for the solution of stochastic inverse problems using a sparse grid collocation approach91 cited
  • 2010Identification of high-dimension polynomial chaos expansions with random coefficients for non-Gaussian tensor-valued random fields using partial and limited experimental data87 cited
  • 2009Polynomial chaos representation of spatio-temporal random fields from experimental measurements

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80 cited
  • 2008Asymptotic Sampling Distribution for Polynomial Chaos Representation from Data: A Maximum Entropy and Fisher Information Approach80 cited
  • 2006Identification of Chaos Representations of Elastic Properties of Random Media Using Experimental Vibration Tests70 cited
  • 2007A probabilistic construction of model validation66 cited
  • 2011A computational inverse method for identification of non-Gaussian random fields using the Bayesian approach in very high dimension61 cited
  • 2008A non-linear dimension reduction methodology for generating data-driven stochastic input models60 cited