  In order to achieve a more virtual design and certification process of jet
engines in aviation industry, the uncertainty bounds for computational fluid
dynamics have to be known. This work shows the application of a machine
learning methodology to quantify the epistemic uncertainties of turbulence
models. The underlying method in order to estimate the uncertainty bounds is
based on an eigenspace perturbation of the Reynolds stress tensor in
combination with random forests.
