The use of model-based simulation in engineering often necessitates the need to estimate model parameters based on physical experiments or field data. This class of problems is referred to as inverse problems in the literature and two significant challenges based on the application of inverse modeling technology to practical engineering problems are (a) computational cost of the inverse solution for complex transient simulation models that needed a long time to execute (b) ability of the instrumentation to shed light on the model parameters being estimated. This paper develops a methodology for the use of transient meta-modeling techniques for data matching applications to address the computational efficiency. The transient meta-models are constructed using the SVD/PCA approach to identify the key transient signature patterns from a dimension reduction perspective. Accuracy of the inverse modeling method with the direct simulation model and the meta-model are compared. The paper concludes with a methodology to optimally design an experiment and collect data to improve the nature of the inverse problem and the confidence with which the model parameters are estimated.

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