Machine learning for locally periodic structure transmission modelling

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F24%3A00378655" target="_blank" >RIV/68407700:21230/24:00378655 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Machine learning for locally periodic structure transmission modelling
Original language description
This research aims to obtain an analytical transmission model for locally periodic structures with the approach of data-driven physics. So far, this task has been done mainly numerically for non-trivial geometries. From a dataset of Bloch phases generated for given frequency band and structure geometry parameters, it is possible to learn equations relating dispersion relation to axis-symmetric geometry. The dataset is transformed into a lower-dimensional space by applying Principal Component Analysis (PCA) to reduce the complexity of the problem. In the newly obtained coordinate system, the lower-dimensional patterns are extracted via symbolic regression. The resulting model is interpretable in terms of underlying physics and can be used, e.g., to propose an optimized design for a desired band gap width. Note that this has been so far possible only with numerical optimization repeatedly going back and forth from geometry to the dispersion relation, and hence, it significantly contributes to the overall readability of the system features.
Czech name
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Czech description
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Project
<a href="/en/project/GA22-33896S" target="_blank" >GA22-33896S: Advanced methods of sound and elastic wave field control: acoustic black holes, metamaterials and functionally graded materials</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Proceedings of the 53rd International Congress and Exposition on Noise Control Engineering, Nantes, France, 25-29 August 2024
ISBN
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ISSN
0736-2935
e-ISSN
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Number of pages
7
Pages from-to
3099-3105
Publisher name
Institute of Noise Control Engineering of the USA
Place of publication
Washington, DC
Event location
Nantes
Event date
Aug 25, 2024
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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