A Computational Inverse Technique for Uncertainty Quantification in an Encounter Condition Identification Problem
W. Zhang1, X. Han1,2, J. Liu1, R. Chen1
CMES-Computer Modeling in Engineering & Sciences, Vol.86, No.5, pp. 385-408, 2012, DOI:10.3970/cmes.2012.086.385
Abstract A novel inverse technique is presented for quantifying the uncertainty of the identified the results in an encounter condition identification problem. In this technique, the polynomial response surface method based on the structure-selection technique is first adopted to construct the approximation model of the projectile/target system, so as to reduce the computational cost. The Markov Chain Monte Carlo method is then used to identify the encounter condition parameters and their confidence intervals based on this cheap approximation model with Bayesian perspective. The results are demonstrated through the simulated test cases, which show the utility and More >
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