AI Article Synopsis
- The authors introduce a method that uses unsupervised machine learning to improve the classification of wave confinement dimensionality, building on their previous work.
- They apply k-means++ and a new model-based algorithm to analyze 3D superlattices of cavities in a photonic band gap crystal and compare the results with direct scaling methods.
- The study finds that using direct scaling first to identify dimensionality, followed by their model-based clustering, yields the most accurate results.
Article Abstract
We propose a rigorous method to classify the dimensionality of wave confinement by utilizing unsupervised machine learning to enhance the accuracy of our recently presented scaling method [Phys. Rev. Lett.129, 176401 (2022)10.1103/PhysRevLett.129.176401]. We apply the standard k-means++ algorithm as well as our own model-based algorithm to 3D superlattices of resonant cavities embedded in a 3D inverse woodpile photonic band gap crystal with a range of design parameters. We compare their results against each other and against the direct usage of the scaling method without clustering. Since the clustering algorithms require the set of confinement dimensionalities present in the system as an input, we investigate cluster validity indices (CVIs) as a means to find these values. We conclude that the most accurate outcome is obtained by first applying direct scaling to find the correct set of confinement dimensionalities, and subsequently utilizing our model-based clustering algorithm to refine the results.
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| http://dx.doi.org/10.1364/OE.492014 | DOI Listing |
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