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. To validate and account for uncertainty in their simulation and modeling results, many companies have started Verification, Validation, and Uncertainty Quantification (VVUQ) initiatives. This webinar will first introduce probabilistic methods and UQ tools, and then use case studies to demonstrate the benefits of VVUQ processes.
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 meaningful results. Probabilistic methods can inform the user on current design robustness as well as avenues for improvement. Using UQ tools, it is also possible to evaluate the degree of uncertainty contributed by the statistical model, quantify confidence in analysis results, and assess how well the model represents reality.
The attendees for this webinar would be engineers, program managers, and data scientists who want to further investigate how Uncertainty Quantification can maximize insight, improve design robustness, and increase time and resource efficiency.
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