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Deep learning for Dirac dispersion engineering in sonic crystals.
- Source :
- Journal of Applied Physics; 6/28/2024, Vol. 135 Issue 24, p1-8, 8p
- Publication Year :
- 2024
-
Abstract
- Band structure and Dirac degeneracy are essential features of sonic crystals/acoustic metamaterials to achieve advanced control of exciting wave effects. In this work, we explore a deep learning approach for the design of phononic crystals with desired dispersion. A plane wave expansion method is utilized to establish the dataset relation between the structural parameters and the energy band features. Subsequently, a multilayer perceptron model trained using the dataset can yield accurate predictions of wave behavior. Based on the trained model, we further impose a re-learning process around a targeted frequency, by which Dirac degeneracy and double Dirac degeneracy can be embedded into the band structures. Our study enables the deep learning approach as a reliable design strategy for Dirac structures/metamaterials, opening up the possibilities for intriguing wave physics associated with Dirac cone. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 00218979
- Volume :
- 135
- Issue :
- 24
- Database :
- Complementary Index
- Journal :
- Journal of Applied Physics
- Publication Type :
- Academic Journal
- Accession number :
- 178147971
- Full Text :
- https://doi.org/10.1063/5.0206258