A Study on the Statistical Model Validation using Response-adaptive Experimental Design

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  • 정병창 (한국기계연구원) ;
  • 허영철 (한국기계연구원 시스템다이나믹스연구실) ;
  • 문석준 (한국기계연구원 시스템다이나믹스연구실) ;
  • 김영중 (한국기계연구원 시스템다이나믹스연구실)
  • Published : 2014.10.29


Model verification and validation (V&V) is a current research topic to build computational models with high predictive capability by addressing the general concepts, processes and statistical techniques. The hypothesis test for validity check is one of the model validation techniques and gives a guideline to evaluate the validity of a computational model when limited experimental data only exist due to restricted test resources (e.g., time and budget). The hypothesis test for validity check mainly employ Type I error, the risk of rejecting the valid computational model, for the validity evaluation since quantification of Type II error is not feasible for model validation. However, Type II error, the risk of accepting invalid computational model, should be importantly considered for an engineered products having high risk on predicted results. This paper proposes a technique named as the response-adaptive experimental design to reduce Type II error by adaptively designing experimental conditions for the validation experiment. A tire tread block problem and a numerical example are employed to show the effectiveness of the response-adaptive experimental design for the validity evaluation.


Supported by : 한국기계연구원