Statistical metamodel of liner acoustic impedance based on neural network and probabilistic learning for small datasets - Mechanics
Article Dans Une Revue Aerospace Année : 2024

Statistical metamodel of liner acoustic impedance based on neural network and probabilistic learning for small datasets

Résumé

The main novelty of this paper consists in presenting a statistical Artificial Neural Network (ANN) based model for a robust prediction of the frequency-dependent aeroacoustic liner impedance using an Aeroacoustic Computational Model (ACM) dataset of small size. The model, focusing on Percentage of Open Area (POA) and Sound Pressure Level (SPL) at a zero Mach number, takes into accounts uncertainties using a probabilistic formulation. The main difficulty in training an ANN-based model is the small size of the ACM dataset. The probabilistic learning carried out using the Probabilistic Learning on Manifold (PLoM) algorithm addresses this difficulty as it allows constructing a very large training dataset from learning the probabilistic model from a small dataset. A prior conditional probability model is presented for the PCA-based statistical reduced representation of the frequency-sampled vector of the logresistance and reactance. It induces some statistical constraints that are not straightforwardly taken into account when training such an ANN-based model by classical optimizations methods under constraints. A second novelty of this paper consists in presenting an alternate solution that involves using conditional statistics estimated with learned realizations from PLoM. A numerical example is presented.
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Dates et versions

hal-04684549 , version 1 (02-09-2024)

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Amritesh Sinha, Christophe Desceliers, Christian Soize, Guilherme Cunha. Statistical metamodel of liner acoustic impedance based on neural network and probabilistic learning for small datasets. Aerospace, 2024, 11 (717), pp.1-14. ⟨10.3390/aerospace11090717⟩. ⟨hal-04684549⟩
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