Webinars

A common approach to assessing the variability in an engineering simulation model is to conduct Uncertainty Propagation. This method represents known input variabilities using probability distributions and then randomly samples from these distributions using Monte Carlo routines. Another method of performing Uncertainty Propagation utilizes predictive models (a.k.a., emulators or surrogate models) to propagate the uncertainties from the inputs.

In addition to propagating input uncertainties, Uncertainty Quantification (UQ) provides a more comprehensive framework for assessing various sources of uncertainties in engineering simulations. This framework includes several key analytics techniques:

• Building predictive models: trained to mimic complex engineering simulations.
• Sensitivity Analysis: ranks parameters by their ability to influence the results.
• Statistical Calibration: handles the disagreement and uncertainty between the simulation model and physical tests.
• Inverse Analysis: determines an underlying distribution for ill-conditioned and sparse model input.

This webinar will discuss and compare sampling-based and emulator-based methods for Uncertainty Propagation. Using example problems and software demonstrations for illustration, the webinar will also show how using additional UQ methods improves the decision-making process. The audience for the webinar includes engineers, data scientists, and managers who want to learn more about the methods and benefits of quantifying uncertainties in their engineering simulations.