Sander van Rijn, Sebastian Schmitt, Matthijs van Leeuwen, Thomas Bäck, "Finding Efficient Trade-offs in Multi-Fidelity Response Surface Modeling ", Engineering optimization, 2022.

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.