Challenges in Uncertainty, Calibration, Validation and Predictability of Engineering Analysis Models

Abstract

Model calibration, validation, prediction and uncertainty quantification have progressed remarkably in the past decade. However, many issues remain. This paper attempts to provide answers to the key questions: 1) how far have we gone? 2) what technical challenges remain? and 3) what are the future directions for this work? Based on a comprehensive literature review from academic, industrial and government research and experience gained at the General Electric (GE) Company, the paper will summarize the advancements of methods and the application of these methods to calibration, validation, prediction and uncertainty quantification. The latest research and application thrusts in the field will emphasize the extension of the Bayesian framework to validation of engineering analysis models. Closing remarks will offer insight into possible technical solutions to the challenges and future research directions.

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