Tuesday  |  Salon 9  |  09:40 AM–10:00 AM

#16925–Analyzing the Influential Factors on ICaF Performance in Bayesian Model Calibration and Forecasting

In the previous work, the authors have proposed an uncertainty-aware metric called information measure of calibration flexibility (ICaF). ICaF addresses the trade-off between goodness-of-fit and model generalizability, and its efficacy has been proved in calibration parameter selection and model selection. This study further investigates the impact of four influential factors on the performance of ICaF through a regression example. These factors include the model form, the selection of calibration parameters, prior knowledge of the system being studied, observation characteristics (i.e., the quantity and distributions of observations), and experimental uncertainty. Models with different model simplicity affect the goodness-of-fit of a calibrated model and the subsequent model predictions. Prior knowledge reflects initial beliefs about the underlying system. The characteristics of observations comprehensively consider the influence of quantity and distributions of observations on the calibration process. Lastly, experimental uncertainty associated with various sources is included. This study provides a comprehensive analysis by systematically varying influential factors and assesses how these factors individually influence the effectiveness of ICaF in model calibration and forecasting. These findings contribute to a collective understanding and insights into the behavior and the robustness of ICaF, which informs the design and implementation of ICaF in scientific and engineering domains.

Xinyue Xu   The Pennsylvania State University
Yishuang Wang   The Pennsylvania State University
Roland Platz   Deggendorf Institute of Technology
Sez Atamturktur   Clemson University