Auflistung nach Schlagwort "Model updating"

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  • Distribution-free stochastic model updating with staircase density functions 
    Kitahara, M.; Kitahara, T.; Bi, S.; Broggi, M.; Beer, M. ([Erscheinungsort nicht ermittelbar] : Taylor & Francis, 2023)
    In stochastic model updating, hybrid uncertainties are typically characterized by the distributional p-box. It assigns a certain probability distribution to model parameters and assumes its hyper-parameters as interval ...
  • Efficient posterior estimation for stochastic SHM using transport maps 
    Grashorn, J.; Broggi, M.; Chamoin, L.; Beer, M. ([Erscheinungsort nicht ermittelbar] : Taylor & Francis, 2023)
    Accurate parameter estimation is a challenging task that demands realistic models and algorithms to obtain the parameter’s probability distributions. The Bayesian theorem in conjunction with sampling methods proved to be ...
  • Model Updating Strategy of the DLR-AIRMOD Test Structure 
    Patelli, E.; Broggi, M.; Govers, Y.; Mottershead, J.E. (London : Elsevier Ltd., 2017)
    Considerable progresses have been made in computer-aided engineering for the high fidelity analysis of structures and systems. Traditionally, computer models are calibrated using deterministic procedures. However, different ...
  • Sensitivity or Bayesian model updating: a comparison of techniques using the DLR AIRMOD test data 
    Patelli, Eduardo; Govers, Yves; Broggi, Matteo; Gomes, Herbert Martins; Link, Michael; Mottershead, John E. (Heidelberg : Springer Verlag, 2017)
    Deterministic model updating is now a mature technology widely applied to large-scale industrial structures. It is concerned with the calibration of the parameters of a single model based on one set of test data. It is, ...
  • Stochastic Model Updating with Uncertainty Quantification: An Overview and Tutorial 
    Bi, Sifeng; Beer, Michael; Cogan, Scott; Mottershead, John (Amsterdam [u.a.] : Elsevier, 2023)
    This paper presents an overview of the theoretic framework of stochastic model updating, including critical aspects of model parameterisation, sensitivity analysis, surrogate modelling, test-analysis correlation, parameter ...