A Forecasting Metric for Predictive Modeling
As the complexity of engineering systems increases, their performance becomes more difficult to predict through modeling and simulation. This paper investigates simulation models used to forecast predictions of the performance of engineering systems in support of high-consequence decision-making. Specifically this paper directs its attention to the validation of the simulation models for certification purposes. Instead of relying on virgin models, i.e. models that are not calibrated or bias-corrected, we envision certification to be applied through a combined experimental and numerical campaign that relies on simulation models calibrated and bias corrected against experimental measurements. We are particularly interested in the quantification and control of errors associated with the forecasting predictions of these calibrated and bias corrected simulation models.
KeywordsForecast Error Discrepancy Bias Gaussian Process Model Unknown Unknown Certification Purpose
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