Multi‐fidelity Gaussian processes for an efficient approximation of frequency sweeps in acoustic problems

Abstract

This paper presents a multi‐fidelity model for the acceleration of frequency sweep analyses in acoustics. In traditional analyses, the frequency‐dependent Helmholtz equation is repetitively solved at each frequency. Using the boundary element method requires then a plethora of evaluations resulting in high computational costs. In the proposed method, the fidelity levels are realized as Gaussian processes, which are conditioned on observations obtained by boundary element simulations. A coarse boundary element mesh is considered as the low‐fidelity model, whereas a fine mesh is adopted as the high‐fidelity model. To validate the proposed framework, the vehicle interior noise problem is investigated. The results demonstrate that multi‐fidelity Gaussian processes efficiently accelerate the frequency sweep analysis, as they provide accurate and fast predictions across the entire frequency range of interest. As a beneficial side effect, the present method takes uncertainties into account. This allows to consider limited information on the model, which is particularly important in early design phases.