Model validation is a process to assess the validity and predictive capabilities of a computer model by comparing simulation results with test data for its intended use of the model. One of the key difficulties for model validation is to evaluate the quality of a computer model at different test configurations in design space, and interpolate or extrapolate the evaluation results to untested new design configurations. In this paper, an integrated model interpolation and extrapolation framework based on Bayesian inference and Response Surface Models (RSM) is proposed to validate the designs both within and outside of the original design space. Bayesian inference is first applied to quantify the distributions' hyper-parameters of the bias between test and CAE data in the validation domain. Then, the hyper-parameters are extrapolated from the design configurations to untested new design. They are then followed by the prediction interval of responses at the new design points. A vehicle design of front impact example is used to demonstrate the proposed methodology.
A Bayesian Inference based Model Interpolation and Extrapolation
Sae Int. J. Mater. Manf
Sae International Journal of Materials and Manufacturing
SAE 2012 World Congress & Exhibition ; 2012
Sae International Journal of Materials and Manufacturing ; 5 , 2 ; 357-364
2012-04-16
8 pages
Conference paper
English
A Bayesian inference based model interpolation and extrapolation
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