This paper presents an enhanced Bayesian based model validation method together with probabilistic principal component analysis (PPCA). The PPCA is employed to address multivariate correlation and to reduce the dimensionality of the multivariate functional responses. The Bayesian hypothesis testing is used to quantitatively assess the quality of a multivariate dynamic system. Unlike the previous approach, the differences between test and CAE results are used for dimension reduction though PPCA and then to assess the model validity. In addition, physics-based thresholds are defined and transformed to the PPCA space for Bayesian hypothesis testing. This new approach resolves some critical drawbacks of the previous method and provides desirable properties of a validation method, e.g., symmetry. A dynamic system with multiple functional responses is used to demonstrate this new approach.


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    Title :

    A Study of Model Validation Method for Dynamic Systems


    Additional title:

    Sae Technical Papers


    Contributors:
    Yang, Ren-Jye (author) / Fu, Yan (author) / Zhan, Zhenfei (author)

    Conference:

    SAE 2010 World Congress & Exhibition ; 2010



    Publication date :

    2010-04-12




    Type of media :

    Conference paper


    Type of material :

    Print


    Language :

    English




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