  In recent years, a great emphasis has been put on engineering the acoustic
signature of vehicles that represents the overall comfort level for passengers.
Due to highly uncertain behavior of production cars, probabilistic metamodels
or surrogates can be useful to estimate the NVH dispersion and assess different
NVH risks. These metamodels follow physical behaviors and shall aid as a design
space exploration tool during the early stage design process to support the NVH
optimization. The measurement databases constitute different noise paths such
as aerodynamic noise (wind-tunnel test), tire-pavement interaction noise
(rolling noise), and noise due to electric motors (whining noise). This
research work proposes a global NVH metamodeling technique for broadband noises
such as aerodynamic and rolling noises exploiting the Bayesian framework that
takes into account the prior (domain-expert) knowledge about complex physical
mechanisms. Generalized additive models (GAMs) with polynomials and Gaussian
basis functions are used to model the dependency of sound pressure level (SPL)
on predictor variables. Moreover, parametric bootstrap algorithm based on
data-generating mechanism using the point estimates is used to estimate the
dispersion in unknown parameters. Probabilistic modelling is carried out using
an open-source library PyMC3 that utilizes No-U-Turn sampler (NUTS) and the
developed models are validated using Cross-Validation technique.
