Uncertainty is an inescapable reality that can be found in nearly all types of engineering analyses. It arises from sources like measurement inaccuracies, material properties, boundary and initial conditions, and modeling approximations. Uncertainty Quantification (UQ) is a systematic process that puts error bands on the results by incorporating real world variability and probabilistic behavior into engineering and systems analysis. UQ answers the question: what is likely to happen when the system is subjected to uncertain and variable inputs. Answering this question facilitates significant risk reduction, robust design, and greater confidence in engineering decisions. Modern UQ techniques use powerful statistical models to map the input-output relationships of the system, significantly reducing the number of simulations or tests required to get accurate answers.
The training will discuss the importance of using UQ from the perspective of industry ROI, regulatory compliance and emerging technologies like the Digital Twin / Digital Thread initiatives. Then the training will present common UQ processes that operate within a probabilistic framework utilized for numerical simulations. The training will use example problems and case studies to illustrate basic UQ concepts. These techniques have been successfully applied in many types of engineering systems for industrial applications.
This training would be ideal for engineers, managers, and data scientists who have interest in learning more on probabilistic analysis or analytics and want to further investigate how UQ can maximize insight, improve design, and reduce time and resources.
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