Modelling band structure properties using physics-informed machine learning

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F25%3A00384829" target="_blank" >RIV/68407700:21230/25:00384829 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.71568/dasdaga2025.605" target="_blank" >http://dx.doi.org/10.71568/dasdaga2025.605</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.71568/dasdaga2025.605" target="_blank" >10.71568/dasdaga2025.605</a>

Result language
angličtina
Original language name
Modelling band structure properties using physics-informed machine learning
Original language description
Although the variety of analytical approaches and numerical methods to solve sonic crystal problems is wide, the known analytical expressions used to model the band structure properties are limited to a few special cases. However, having access to a numerical model is a good starting point for data-driven discovery. Our approach employed the Webster equation for unit cell and Floquet-Bloch theory for periodic structures with waveguide parametrized by cubic splines. Analytical formulae relating the waveguide geometry to the corresponding dispersion relation were extracted using methods of physics-informed machine learning, such as coordinate transformation and symbolical regression. These results provide a deeper understanding of the underlying principles and offer an efficient alternative to computationally demanding numerical optimization. Moving towards a Schrödinger-like equation and parametrization by Gaussian curvature allows for a more multiphysical approach but also faces some challenges in terms of geometry feasibility limits.
Czech name
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Czech description
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Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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