  In many fields of science, comprehensive and realistic computational models
are available nowadays. Often, the respective numerical calculations call for
the use of powerful supercomputers, and therefore only a limited number of
cases can be investigated explicitly. This prevents straightforward approaches
to important tasks like uncertainty quantification and sensitivity analysis.
This challenge can be overcome via our recently developed sensitivity-driven
dimension adaptive sparse grid interpolation strategy. The method exploits, via
adaptivity, the structure of the underlying model (such as lower intrinsic
dimensionality and anisotropic coupling of the uncertain inputs) to enable
efficient and accurate uncertainty quantification and sensitivity analysis at
scale. We demonstrate the efficiency of our approach in the context of fusion
research, in a realistic, computationally expensive scenario of turbulent
transport in a magnetic confinement tokamak device with eight uncertain
parameters, reducing the effort by at least two orders of magnitude. In
addition, we show that our method intrinsically provides an accurate surrogate
model that is nine orders of magnitude cheaper than the high-fidelity model.
