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Sfoglia per Soggetto "Stochastic simulation algorithms"

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      Bayesian Hierarchical Models for Uncertainty Quantification in Structural Dynamics 

      Ballesteros, G. C.; Angelikopoulos, P.; Papadimitriou, C.; Koumoutsakos, P. (2014)
      The Bayesian framework for hierarchical modeling is applied to quantify uncertainties, arising mainly due to manufacturing variability, for a group of identical structural components. Parameterized Gaussian models of ...
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      Bayesian uncertainty quantification and propagation in nonlinear structural dynamics 

      Giagopoulos, D.; Papadioti, D. C.; Papadimitriou, C.; Natsiavas, S. (2013)
      A Bayesian uncertainty quantification and propagation (UQ&P) framework is presented for identifying nonlinear models of dynamic systems using vibration measurements of their components. The measurements are taken to be ...
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      Efficient techniques for bayesian inverse modeling of large-order computational models 

      Papadimitriou, C.; Angelikopoulos, P.; Koumoutsakos, P.; Papadioti, D. C. (2013)
      Bayesian tools for inverse modeling are based on asymptotic approximations and Stochastic Simulation Algorithms (SSA). Such tools require a number of moderate to large number of system re-analyses. For large-order numerical ...
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      Uncertainty calibration of large-order models of bridges using ambient vibration measurements 

      Papadimitriou, C.; Argyris, C.; Papadioti, D. C.; Panetsos, P. (2014)
      A computational efficient Bayesian inference framework based on stochastic simulation algorithms is presented for calibrating the parameters of large-order linear finite element (FE) models of bridges. The effectiveness ...
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