dc.contributor.author | De La Vega Martin, C | |
dc.contributor.author | Sandler, M | |
dc.contributor.author | Forum Acusticum 2023 | |
dc.date.accessioned | 2023-10-13T09:00:31Z | |
dc.date.available | 2023-07-19 | |
dc.date.available | 2023-10-13T09:00:31Z | |
dc.date.issued | 2023 | |
dc.identifier.uri | https://qmro.qmul.ac.uk/xmlui/handle/123456789/91302 | |
dc.description.abstract | Physical modelling synthesis is currently limited in its ap- plications due to the high computational cost of some of the algorithms. Typically, these models are obtained by discretizing a mathematical model described by ordinary or partial differential equations, using well-established methods like finite differences or modal decomposition. Recent advances in machine-learning have sought to model these systems of differential equations using spe- cialised architectures such as the Fourier Neural Opera- tor (FNO), enabling extremely fast inference times and resolution-independent computational cost. Building on recent work extending the FNO approach for its applica- tion to acoustics problems, we examine the performance and robustness of these methods in the case of a stiff and lossy membrane. We show that the FNO approach is only able to accurately model the behaviour of the membrane within the range of timesteps used for training, becoming unstable or decaying rapidly beyond that. | en_US |
dc.publisher | Forum Acusticum 2023 | en_US |
dc.rights | This item is distributed under the terms of the Creative Commons Attribution 3.0 Unported License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. | |
dc.title | Physical Modelling of Stiff Membrane Vibration using Neural Networks with Spectral Convolution Layers | en_US |
dc.type | Conference Proceeding | en_US |
dc.rights.holder | © 2023, The Author(s) | |
pubs.notes | Not known | en_US |
pubs.publication-status | Published online | en_US |
dcterms.dateAccepted | 2023-07-19 | |
rioxxterms.funder | Default funder | en_US |
rioxxterms.identifier.project | Default project | en_US |
qmul.funder | UKRI Centre for Doctoral Training in Artificial Intelligence and Music::Engineering and Physical Sciences Research Council | en_US |