@article {pub4415pub4641,
	title = {Finding Efficient Trade-offs in Multi-Fidelity Response Surface Modeling
},
	author = {Sander van Rijn AND Sebastian Schmitt AND Matthijs  van Leeuwen AND Thomas B{\"a}ck},
	year = {2022},
	abstract = { In the context of optimization approaches to
engineering applications, time-consuming simulations
are often utilized which can be configured to deliver solu-
tions 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 correla-
tion between the high- and low-fidelity versions of the
problem that can be exploited to cheaply gain informa-
tion. In the practical scenario where the computational
budget has to be allocated between multiple fidelities,
limited guidelines are available to help make that di-
vision. In this paper we evaluate a range of different
choices for a two-fidelity setup that provide helpful intu-
itions about the trade-off between evaluating in high- or
low-fidelity. We present a heuristic method based on sub-
sampling 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.},
	publisher = {Taylor \& Francis Group},
	journal = {Engineering optimization}
}