  The study of complex systems is often based on computationally intensive,
high-fidelity, simulations. To build confidence in the prediction accuracy of
such simulations, the impact of uncertainties in model inputs on the quantities
of interest must be measured. This, however, requires a computational budget
that is a possibly large multiple of the cost of a single simulation, and thus
may become infeasible for expensive simulation models featuring a large number
of uncertain inputs and highly nonlinear behavior. Therefore, this work
explores multi-fidelity strategies to accelerate the estimation of the effect
of uncertainties. The main idea behind multi-fidelity models is to utilize
cheaper, lower-fidelity models - than the intended high-fidelity, expensive
model of the problem - to generate a baseline solution that together, with
relatively small number of high-fidelity simulations can lead to accurate
predictions. The methods are briefly presented, and their performance assessed
on an irradiated particle-laden turbulent flow case related to Stanford's PSAAP
II particle-based solar energy receiver.
