Abstract

Model calibration is the process of estimating unknown inputs in a model to improve the agreement between model predictions and experimental observations. Optimization-based model calibration is a probabilistic approach for estimating unknown inputs by using optimization techniques. Gradient-based optimization algorithms are popular for optimization-based model calibration because of their computational efficiency. Gradient-based algorithms, however, also have drawbacks that include the local optimum issue, the numerical noise issue, lack of gradient information, and related concerns. In optimization-based model calibration, a calibration metric that quantifies the similarity or difference between two probability distributions (the predicted and the observed system responses) is defined as an objective function. Current methods of optimization-based model calibration use existing calibration metrics, such as the likelihood function and the probability residual. Occasionally, these methods show inaccurate calibrated results. Therefore, first, this comprehensive study investigates the root causes of the inaccurate calibrated results that arise from using existing calibration metrics. Second, an enhanced method is proposed to achieve robust optimization-based model calibration by providing analytical gradient information. This study provides a general guideline for improved optimization-based model calibration.