In this paper, different approaches to parameter calibration and model validation were compared to understand the accuracy and robustness, especially when only a small number of data are available. Conventional one-point calibration, two-point calibration, sensitivity-based calibration, discrepancy-based calibration methods are compared when the number of data is less than three. An analytical example as well as a cantilever beam model are used to demonstrate the performance and accuracy of different methods. Numerical examples indicate that the conventional calibration method that does not account for the discrepancy function may lead to biased parameter and prediction models. It also can be seen that accurate parameter can be identified only when the form of discrepancy function is accurate.

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