By Mark Andrews, Ph.D.
Numerical simulations have become the choice approach for performing analytics in many industrial sectors. With the phenomenal growth in computational power and significant advancements made in Computer-Aided Engineering (CAE) software, computer experiments of complex systems are now capable of reducing the dependency and costs of conducting physical experiments. While the prevalence of simulation tools offers unique potential to generate expedient analytics, simulation modeling of complex systems requires Uncertainty Quantification, an advanced analytical methodology capable of generating actionable results.
Uncertainty Quantification is a multi-disciplinary field that brings together statistics, applied mathematics, and computer science to quantify uncertainties in numerical simulations. Like Six Sigma, Uncertainty Quantification makes use of statistical models to find feasible solutions to problems involving variability. However, the two methodologies seek to meet different objectives.
This webinar will begin by introducing the topic of Uncertainty Quantification along with the basic methods and processes used to quantify uncertainties. Illustrative examples will be used to highlight how UQ can enhance Six Sigma.