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288439

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

Article

Last updated: 05 Jan 2025

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-

Tags

Physics

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.

DOI

10.21608/ejaps.2023.181263.1047

Keywords

Deep learning, optical detector, Waveguides

Authors

First Name

Omar

Last Name

Elsheikh

MiddleName

-

Affiliation

Department of Physics, School of Sciences and Engineering, American University, Cairo, Egypt.

Email

omarelsheikh@aucegypt.edu

City

-

Orcid

-

First Name

Adel

Last Name

Shaaban

MiddleName

-

Affiliation

National Center for Radiation Research and Technology (NCRRT), Egyptian Atomic Energy Authority, Cairo, Egypt

Email

engadelsas@gmail.com

City

-

Orcid

-

First Name

Amany

Last Name

Arafa

MiddleName

-

Affiliation

National Center for Radiation Research and Technology (NCRRT), Egyptian Atomic Energy Authority, Cairo, Egypt

Email

amany_arafa@hotmail.com

City

-

Orcid

-

First Name

Ashraf

Last Name

Yahia

MiddleName

Shams Eldien

Affiliation

Physics Department, Faculty of Science, Ain Shams University, Cairo, Egypt

Email

ayahia@sci.asu.edu.eg

City

Cairo

Orcid

0000-0003-1998-4647

First Name

Lotfy

Last Name

Gomaa

MiddleName

-

Affiliation

Faculty of Engineering at Shobra, Banha University, Cairo, Egypt

Email

lotfigomaa@gmail.com

City

-

Orcid

-

First Name

Nasr

Last Name

Gad

MiddleName

-

Affiliation

Physics Department, Faculty of Science, Ain Shams University, Cairo, Egypt

Email

ngad@sci.asu.edu.eg

City

Cairo

Orcid

0000-0002-4175-877X

Volume

61

Article Issue

1

Related Issue

38602

Issue Date

2023-01-01

Receive Date

2022-12-17

Publish Date

2023-01-29

Page Start

1

Page End

10

Print ISSN

2090-231X

Online ISSN

2786-0299

Article File

EJAPS_Volume 61_Issue 1_Pages 1-10.pdf

PDF . 1.6MB

Link

https://ejpasa.journals.ekb.eg/article_288439.html

Detail API

https://ejpasa.journals.ekb.eg/service?article_code=288439

Order

1

Type

Original Article

Type Code

1,912

Publication Type

Journal

Publication Title

Egyptian Journal of Pure and Applied Science

Publication Link

https://ejpasa.journals.ekb.eg/

MainTitle

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

Details

Type

Article

Created At

28 Dec 2024