Modelling of superposition in 2D linear acoustic wave problems using Fourier neural operator networks
A method of solving the 2D acoustic wave equation using Fourier Neural Operator (FNO) networks is presented. Various scenarios including wave superposition are considered, including the modelling of multiple simultaneous sound sources, reflections from domain boundaries and diffraction from randomly...
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EDP Sciences
2025-01-01
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| Series: | Acta Acustica |
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| Online Access: | https://acta-acustica.edpsciences.org/articles/aacus/full_html/2025/01/aacus240111/aacus240111.html |
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| author | Middleton Michael Murphy Damian T. Savioja Lauri |
| author_facet | Middleton Michael Murphy Damian T. Savioja Lauri |
| author_sort | Middleton Michael |
| collection | DOAJ |
| description | A method of solving the 2D acoustic wave equation using Fourier Neural Operator (FNO) networks is presented. Various scenarios including wave superposition are considered, including the modelling of multiple simultaneous sound sources, reflections from domain boundaries and diffraction from randomly-positioned and sized rectangular objects. Training, testing and ground-truth data is produced using the acoustic Finite-Difference Time-Domain (FDTD) method. FNO is selected as the neural architecture as the network architecture requires relatively little memory compared to some other operator network designs. The number of training epochs and the size of training datasets were chosen to be small to test the convergence properties of FNO in challenging learning conditions. FNO networks are shown to be time-efficient means of simulating wave propagation in a 2D domain compared to FDTD, operating 25 × faster in some cases. Furthermore, the FNO network is demonstrated as an effective means of data compression, storing a 24.4 GB training dataset as a 15.5 MB set of network weights. |
| format | Article |
| id | doaj-art-e40c08fd96c54145b45acb3883af5fbd |
| institution | Kabale University |
| issn | 2681-4617 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | Acta Acustica |
| spelling | doaj-art-e40c08fd96c54145b45acb3883af5fbd2025-08-20T03:42:25ZengEDP SciencesActa Acustica2681-46172025-01-0192010.1051/aacus/2024078aacus240111Modelling of superposition in 2D linear acoustic wave problems using Fourier neural operator networksMiddleton Michael0https://orcid.org/0000-0002-0862-7524Murphy Damian T.1https://orcid.org/0000-0002-6676-9459Savioja Lauri2https://orcid.org/0000-0002-8261-4596AudioLab, School of Physics, Engineering and Technology, University of YorkAudioLab, School of Physics, Engineering and Technology, University of YorkDepartment of Computer Science, Acoustics Lab, Aalto UniversityA method of solving the 2D acoustic wave equation using Fourier Neural Operator (FNO) networks is presented. Various scenarios including wave superposition are considered, including the modelling of multiple simultaneous sound sources, reflections from domain boundaries and diffraction from randomly-positioned and sized rectangular objects. Training, testing and ground-truth data is produced using the acoustic Finite-Difference Time-Domain (FDTD) method. FNO is selected as the neural architecture as the network architecture requires relatively little memory compared to some other operator network designs. The number of training epochs and the size of training datasets were chosen to be small to test the convergence properties of FNO in challenging learning conditions. FNO networks are shown to be time-efficient means of simulating wave propagation in a 2D domain compared to FDTD, operating 25 × faster in some cases. Furthermore, the FNO network is demonstrated as an effective means of data compression, storing a 24.4 GB training dataset as a 15.5 MB set of network weights.https://acta-acustica.edpsciences.org/articles/aacus/full_html/2025/01/aacus240111/aacus240111.htmlmachine learningneural networkslinear acousticsreflectionsgeneralisation |
| spellingShingle | Middleton Michael Murphy Damian T. Savioja Lauri Modelling of superposition in 2D linear acoustic wave problems using Fourier neural operator networks Acta Acustica machine learning neural networks linear acoustics reflections generalisation |
| title | Modelling of superposition in 2D linear acoustic wave problems using Fourier neural operator networks |
| title_full | Modelling of superposition in 2D linear acoustic wave problems using Fourier neural operator networks |
| title_fullStr | Modelling of superposition in 2D linear acoustic wave problems using Fourier neural operator networks |
| title_full_unstemmed | Modelling of superposition in 2D linear acoustic wave problems using Fourier neural operator networks |
| title_short | Modelling of superposition in 2D linear acoustic wave problems using Fourier neural operator networks |
| title_sort | modelling of superposition in 2d linear acoustic wave problems using fourier neural operator networks |
| topic | machine learning neural networks linear acoustics reflections generalisation |
| url | https://acta-acustica.edpsciences.org/articles/aacus/full_html/2025/01/aacus240111/aacus240111.html |
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