Machine learning for locally periodic structure transmission modelling

Kód výsledku v 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>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Machine learning for locally periodic structure transmission modelling

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Machine learning for locally periodic structure transmission modelling

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10307 - Acoustics

Projekt

<a href="/cs/project/GA22-33896S" target="_blank" >GA22-33896S: Pokročilé metody řízení zvukových a elastických vlnových polí: akustické černé díry, metamateriály a funkčně gradované materiály</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

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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Počet stran výsledku

7

Strana od-do

3099-3105

Název nakladatele

Institute of Noise Control Engineering of the USA

Místo vydání

Washington, DC

Místo konání akce

Nantes

Datum konání akce

25. 8. 2024

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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