  We analyse and compare various empirical models of wall pressure spectra
beneath turbulent boundary layers and propose an alternative machine learning
approach using Artificial Neural Networks (ANN). The analysis and the training
of the ANN are performed on data from experiments and high-fidelity simulations
by various authors, covering a wide range of flow conditions. We present a
methodology to extract all the turbulent boundary layer parameters required by
these models, also considering flows experiencing strong adverse pressure
gradients. Moreover, the database is explored to unveil important dependencies
within the boundary layer parameters and to propose a possible set of features
from which the ANN should predict the wall pressure spectra. The results show
that the ANN outperforms traditional models in adverse pressure gradients, and
its predictive capabilities generalise better over the range of investigated
conditions. The analysis is completed with a deep ensemble approach for
quantifying the uncertainties in the model prediction and integrated gradient
analysis of the model sensitivity to its inputs. Uncertainties and
sensitivities allow for identifying the regions where new training data would
be most beneficial to the model's accuracy, thus opening the path towards a
self-calibrating modelling approach.
