A Feedforward Neural Network for Modeling of Average Pressure Frequency Response
Artikel i vetenskaplig tidskrift, 2022

The Helmholtz equation has been used for modeling the sound pressure field under a harmonic load. Computing harmonic sound pressure fields by means of solving Helmholtz equation can quickly become unfeasible if one wants to study many different geometries for ranges of frequencies. We propose a machine learning approach, namely a feedforward dense neural network, for computing the average sound pressure over a frequency range. The data are generated with finite elements, by numerically computing the response of the average sound pressure, by an eigenmode decomposition of the pressure. We analyze the accuracy of the approximation and determine how much training data is needed in order to reach a certain accuracy in the predictions of the average pressure response.

Frequency response

Sound pressure

Machine learning

Helmholtz equation

Feedforward dense neural network

Författare

Klas Pettersson

Chalmers, Mikroteknologi och nanovetenskap, Kvantteknologi

Andrei Karzhou

Universitetet i Tromsø – Norges arktiske universitet

Irina Pettersson

Chalmers, Matematiska vetenskaper, Analys och sannolikhetsteori

Göteborgs universitet

Acoustics Australia

0814-6039 (ISSN) 18392571 (eISSN)

Vol. 50 2 185-201

Ämneskategorier

Teknisk mekanik

Beräkningsmatematik

Strömningsmekanik och akustik

DOI

10.1007/s40857-021-00259-w

Mer information

Senast uppdaterat

2024-03-07