دورية أكاديمية
Predicting strongly localized resonant modes of light in disordered arrays of dielectric scatterers: a machine learning approach.
| العنوان: | Predicting strongly localized resonant modes of light in disordered arrays of dielectric scatterers: a machine learning approach. |
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| المؤلفون: | Ali M, Haque AKMN, Sadik N, Ahmed T, Baten MZ |
| المصدر: | Optics express [Opt Express] 2023 Jan 16; Vol. 31 (2), pp. 826-842. |
| نوع المنشور: | Journal Article |
| اللغة: | English |
| بيانات الدورية: | Publisher: Optica Publishing Group Country of Publication: United States NLM ID: 101137103 Publication Model: Print Cited Medium: Internet ISSN: 1094-4087 (Electronic) Linking ISSN: 10944087 NLM ISO Abbreviation: Opt Express Subsets: PubMed not MEDLINE; MEDLINE |
| أسماء مطبوعة: | Publication: Washington, DC : Optica Publishing Group Original Publication: Washington, DC : Optical Society of America, 1997- |
| مستخلص: | In this work, we predict the most strongly confined resonant mode of light in strongly disordered systems of dielectric scatterers employing the data-driven approach of machine learning. For training, validation, and test purposes of the proposed regression architecture-based deep neural network (DNN), a dataset containing resonant characteristics of light in 8,400 random arrays of dielectric scatterers is generated employing finite difference time domain (FDTD) analysis technique. To enhance the convergence and accuracy of the overall model, an auto-encoder is utilized as the weight initializer of the regression model, which contains three convolutional layers and three fully connected layers. Given the refractive index profile of the disordered system, the trained model can instantaneously predict the Anderson localized resonant wavelength of light with a minimum error of 0.0037%. A correlation coefficient of 0.95 or higher is obtained between the FDTD simulation results and DNN predictions. Such a high level of accuracy is maintained in inhomogeneous disordered media containing Gaussian distribution of diameter of the scattering particles. Moreover, the prediction scheme is found to be robust against any combination of diameters and fill factors of the disordered medium. The proposed model thereby leverages the benefits of machine learning for predicting the complex behavior of light in strongly disordered systems. |
| تواريخ الأحداث: | Date Created: 20230214 Date Completed: 20230214 Latest Revision: 20230214 |
| رمز التحديث: | 20240628 |
| DOI: | 10.1364/OE.475495 |
| PMID: | 36785131 |
| قاعدة البيانات: | MEDLINE |
Full Text Finder | تدمد: | 1094-4087 |
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| DOI: | 10.1364/OE.475495 |