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This study evaluates the efficacy of two machine learning (ML) techniques,namely artificial neural networks (ANN) and gene expression programming (GEP)that use data-driven modeling to predict wall pressure spectra (WPS) underneathturbulent boundary layers. Different datasets of WPS from experiments andhigh-fidelity numerical simulations covering a wide range of pressure gradientsand Reynolds numbers are considered. For both ML methods, an optimalhyperparameter environment is identified that yields accurate predictions. ANNis observed to be faster and more accurate than GEP with an order of magnitudelower training time and logarithmic mean squared error ($lMSE$), despite ahigher memory consumption. Novel training schemes are devised to address theshortcomings of GEP. These include (a) ANN-assisted GEP to reduce the noise inthe training data, (b) exploiting the low and high-frequency trends to guidethe GEP search, and (c) a stepped training strategy where the chromosomes arefirst trained on the canonical datasets followed by the datasets with complexfeatures. When compared to the baseline scheme, these training strategiesaccelerated convergence and resulted in models with superior accuracy ($\approx30\%$ reduction in the median $lMSE$) and higher reliability ($\approx 75\%$reduction in the spread of $lMSE$ in the interquartile range). The final GEPmodels captured the complex trends of WPS across varying flow conditions andpressure gradients, surpassing the accuracy of Goody's model.

Artificial Neural Networks and Guided Gene Expression Programming to Predict Wall Pressure Spectra Beneath Turbulent Boundary Layers