Predicting Fundamental Transverse Electric Mode of Slab Waveguide Based on Physics-Informed Neural Networks
Elsheikh, O., Shaaban, A., Arafa, A., Yahia, A., Gomaa, L., Gad, N. (2023). Predicting Fundamental Transverse Electric Mode of Slab Waveguide Based on Physics-Informed Neural Networks. EKB Journal Management System, 61(1), 1-10. doi: 10.21608/ejaps.2023.181263.1047
Omar Elsheikh; Adel Shaaban; Amany Arafa; Ashraf Shams Eldien Yahia; Lotfy Gomaa; Nasr Gad. "Predicting Fundamental Transverse Electric Mode of Slab Waveguide Based on Physics-Informed Neural Networks". EKB Journal Management System, 61, 1, 2023, 1-10. doi: 10.21608/ejaps.2023.181263.1047
Elsheikh, O., Shaaban, A., Arafa, A., Yahia, A., Gomaa, L., Gad, N. (2023). 'Predicting Fundamental Transverse Electric Mode of Slab Waveguide Based on Physics-Informed Neural Networks', EKB Journal Management System, 61(1), pp. 1-10. doi: 10.21608/ejaps.2023.181263.1047
Elsheikh, O., Shaaban, A., Arafa, A., Yahia, A., Gomaa, L., Gad, N. Predicting Fundamental Transverse Electric Mode of Slab Waveguide Based on Physics-Informed Neural Networks. EKB Journal Management System, 2023; 61(1): 1-10. doi: 10.21608/ejaps.2023.181263.1047

Predicting Fundamental Transverse Electric Mode of Slab Waveguide Based on Physics-Informed Neural Networks

Egyptian Journal of Pure and Applied Science
Article 1, Volume 61, Issue 1, January 2023, Page 1-10  XML PDF (1.54 MB)
Document Type: Original Article
DOI: 10.21608/ejaps.2023.181263.1047
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Authors
Omar Elsheikh1; Adel Shaaban2; Amany Arafa2; Ashraf Shams Eldien Yahiaorcid 3; Lotfy Gomaa4; Nasr Gad email orcid 3
1Department of Physics, School of Sciences and Engineering, American University, Cairo, Egypt.
2National Center for Radiation Research and Technology (NCRRT), Egyptian Atomic Energy Authority, Cairo, Egypt
3Physics Department, Faculty of Science, Ain Shams University, Cairo, Egypt
4Faculty of Engineering at Shobra, Banha University, Cairo, Egypt
Abstract
Over the past few years, deep learning has proven to be an effective and fast tool in many areas, especially in the field of photonics. The design of integrated optical devices relies on optical waveguides which requires a reliable and fast methods for determining the waveguide’s characteristics before the fabrication process. In this work, the newly emerging paradigm of physics-informed neural networks (PINNs) is employed for analyzing and predicting the fundamental transverse electric (TE) mode and effective refractive index ( ) of a slab waveguide. PINNs is particularly useful as it is a data and mesh-free method, which solve the most critical problems of computational modeling which are the speed and computational hardware cost. The suggested model has a prediction accuracy of up to 99% and effective refractive index relative error ranges between 10-5 and 10-6. Model results are validated against finite difference time domain method using Lumerical software and variational method.
Keywords
Deep learning; optical detector; waveguides
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