Uncertainty Quantification (UQ) is a set of Machine Learning (ML) methods that puts error bands on 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 predictive models to map the input-output relationships of the system, significantly reducing the number of simulations or tests required to get statistically defensible answers.
However, applying ML to engineering problems poses several major challenges. For example, many engineering simulations are deterministic, but the underlying problems they model are subject to uncertainties and, therefore, are stochastic in nature. Although ML may produce an optimal solution, it could be one that corresponds to an unrealistic scenario rather than the desired solution incorporating real-world uncertainty. To achieve its true aim, the ML model must be trained in the stochastic nature of the outcomes of interest by incorporating uncertainty into its decision rules. Other challenges include how to understand uncertainties in ML models themselves and how to build such models for sparse or small data sets or data sets with many inputs.
This course will provide an introduction to ML, with particular focus on those tools and techniques required for UQ. There are no prerequisites, and a refresher of required statistics basics will be included. Challenges and solutions to the application of ML to engineering problems will be addressed. Points will be illustrated with examples utilizing SmartUQ software (e.g. NACA airfoil CFD simulation data).
