A multi-fidelity Gaussian process for efficient frequency sweeps in the acoustic design of a vehicle cabin

Highly accurate predictions from large-scale numerical simulations are associated with increased computational resources and time expense. Consequently, the data generation process can only be performed for a small sample size, limiting a detailed investigation of the underlying system. The concept of multi-fidelity modeling allows the combination of data from different models of varying costs and complexities. This study introduces a multi-fidelity model for the acoustic design of a vehicle cabin. Therefore, two models with different fidelity levels are used to solve the Helmholtz equation at specified frequencies with the boundary element method. Gaussian processes (GPs) are trained on each fidelity level with the simulation results to predict the unknown system response. In this way, the multi-fidelity model enables an efficient approximation of the frequency sweep for acoustics in the frequency domain. Additionally, the proposed method inherently considers uncertainties due to the data generation process. To demonstrate the effectiveness of our framework, the multifrequency solution is validated with the high-fidelity (HF) solution at each frequency. The results show that the frequency sweep is efficiently approximated by using only a limited number of HF simulations. Thus, these findings indicate that multi-fidelity GPs can be adopted for fast and, simultaneously, accurate predictions.