A Feedforward Neural Network for Modeling of Average Pressure Frequency Response
Journal article, 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

Author

Klas Pettersson

Chalmers, Microtechnology and Nanoscience (MC2), Quantum Technology

Andrei Karzhou

University of Tromsø – The Arctic University of Norway

Irina Pettersson

Chalmers, Mathematical Sciences, Analysis and Probability Theory

University of Gothenburg

Acoustics Australia

0814-6039 (ISSN) 18392571 (eISSN)

Vol. 50 2 185-201

Subject Categories

Applied Mechanics

Computational Mathematics

Fluid Mechanics and Acoustics

DOI

10.1007/s40857-021-00259-w

More information

Latest update

3/7/2024 9