As computer models become more powerful and popular, the complexity of input and output data raises new computational challenges. One of the key difficulties for model validation is to evaluate the quality of a computer model with multivariate, highly correlated and non-normal data, the direct application of traditional validation approaches does not appear to be suitable. This paper proposes a stochastic method to validate the dynamic systems. Firstly, a dimension reduction utilizing kernel principal component analysis (KPCA) is used to improve the computational efficiency. A probability model is then established by non-parametric kernel density estimation (KDE) method, and differences between the test data and simulation results are finally extracted to further comparative validation. This new approach resolves some critical drawbacks of the previous methods and improves the processing ability to nonlinear problem to validation the dynamic model. The proposed method and process are successfully illustrated through a real-world vehicle dynamic system example. The results demonstrate that the method of incorporate with KPCA and KDE is an effective approach to solve the dynamic model validation problem.

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