  This work suggests several methods of uncertainty treatment in multiscale
modelling and describes their application to a system of coupled turbulent
transport simulations of a tokamak plasma. We propose a method to quantify the
usually aleatoric uncertainty of a system in a quasi-stationary state,
estimating the mean values and their errors for quantities of interest, which
is average heat fluxes in the case of turbulence simulations. The method
defines the stationarity of the system and suggests a way to balance the
computational cost of simulation and the accuracy of estimation. This allows,
contrary to many approaches, to incorporate aleatoric uncertainties in the
analysis of the model and to have a quantifiable decision for simulation
runtime. Furthermore, the paper describes methods for quantifying the epistemic
uncertainty of a model and the results of such a procedure for turbulence
simulations, identifying the model's sensitivity to particular input parameters
and sensitivity to uncertainties in total. Finally, we introduce a surrogate
model approach based on Gaussian Process Regression and present a preliminary
result of training and analysing the performance of such a model based on
turbulence simulation data. Such an approach shows a potential to significantly
decrease the computational cost of the uncertainty propagation for the given
model, making it feasible on current HPC systems.
