UncertaintyQuantification.jl: Efficient uncertainty propagation powered by Julia
Jasper Behrensdorf, Ander Gray
This talk introduces UncertaintyQuantification.jl , a generalised framework for uncertainty quantification. The framework has undergone extensive development since its initial release in August of 2020 and now includes a number of numerical algorithms to quantify and propagate uncertainties. We have previously presented the package at two scientific conferences and now want to share it with the wider Julia community.
In this talk we discuss a significant subset of the features currently available. Adequate illustrative numerical examples from various engineering disciplines are presented throughout, to highlight the capabilities of the implemented algorithms.
Error, derivatives, stability
Amphithéâtre Jean-Baptiste Say