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Abstract Computer model calibration typically operates by fine-tuning parameter values in a computer model so that the model output faithfully predicts reality. By using performance targets in place of observed data, we show that calibration techniques can be repurposed for solving multi-objective design problems. Our approach allows us to consider all relevant sources of uncertainty as an integral part of the design process. We demonstrate our proposed approach through both simulation and fine-tuning material design settings to meet performance targets for a wind turbine blade. 

Abstract Calibration of computer models and the use of those design models are two activities traditionally carried out separately. This paper generalizes existing Bayesian inverse analysis approaches for computer model calibration to present a methodology combining calibration and design in a unified Bayesian framework. This provides a computationally efficient means to undertake both tasks while quantifying all relevant sources of uncertainty. Specifically, compared with the traditional approach of design using parameter estimates from previously completed model calibration, this generalized framework inherently includes uncertainty from the calibration process in the design procedure. We demonstrate our approach to the design of a vibration isolation system. We also demonstrate how, when adaptive sampling of the phenomenon of interest is possible, the proposed framework may select new sampling locations using both available real observations and the computer model. This is especially useful when a misspecified model fails to reflect that the calibration parameter is functionally dependent upon the design inputs to be optimized. 

Previous research has shown that initial mathematics course placement in college is a strong predictor of persistence to an engineering degree. This study examines whether greater access to devices used in high school STEM courses is positively related to a student’s college math course placement. Both qualitative and quantitative data were collected and analyzed. In the quantitative analysis, data on freshmen in Engineering and Engineering-related programs from across 20 public institutions within the same state revealed that classrooms with wireless access and the number of devices dedicated for student use in their high schools were not useful predictors of their math course placement in college. This runs counter to intuition and may provide new insight into the effectiveness of technology implementation within high school classrooms. In a qualitative analysis, the type of devices, frequency, and manner in which the devices were implemented in high school math courses were examined.