Multi-fidelity modeling using Gaussian processes for the frequency dependent Helmholtz equation

Abstract

In scientific and engineering problems, accurate model predictions are generally accompanied with high resource and time expenses. In most applications, the data generation process can only be performed for a limited number of forward evaluations leading to insufficient model results. The concept of multi-fidelity modeling is to holistically evaluate data from different sources with varying complexity and costs. Ideally, the predictive quality of models with a high-fidelity can be combined with the efficiency of low-fidelity models. Usually, physical experiments and numerical simulations at large-scale are attributed to high-fidelity models, while analytical solutions or small-scale numerical models and experiments are considered as low-fidelity models. This contribution aims to develop a multi-fidelity model for an interior sound propagation problem. More specifically, a vehicle interior noise problem is investigated by using two boundary element models of different fidelity levels. The finer mesh corresponds to the high-fidelity model, whereas the coarser mesh is adopted as the low-fidelity model. As such, the multi-fidelity model is realized as a vector-valued Gaussian process.