  In the context of optimization approaches to engineering applications,
time-consuming simulations are often utilized which can be configured to
deliver solutions for various levels of accuracy, commonly referred to as
different fidelity levels. It is common practice to train hierarchical
surrogate models on the objective functions in order to speed-up the
optimization process. These operate under the assumption that there is a
correlation between the high- and low-fidelity versions of the problem that can
be exploited to cheaply gain information. In the practical scenario where the
computational budget has to be allocated between multiple fidelities, limited
guidelines are available to help make that division. In this paper we evaluate
a range of different choices for a two-fidelity setup that provide helpful
intuitions about the trade-off between evaluating in high- or low-fidelity. We
present a heuristic method based on subsampling from an initial Design of
Experiments (DoE) to find a suitable division of the computational budget
between the fidelity levels. This enables the setup of multi-fidelity
optimizations which utilize the available computational budget efficiently,
independent of the multi-fidelity model used.
