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GENETIC ALGORITHM WITH DEEP NEURAL NETWORK SURROGATE FOR THE OPTIMIZATION OF ELECTROMAGNETIC STRUCTURE

In this current technological era, engineering design process encounters with multiple complexity as it depends on computer simulation to ensure accurate model before the model is actually constructed. This leads to the problems where the engineers have to feed their simulation with new inputs...

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Main Author: MOHAMMED SHARIFF, NUR ATIQAH
Format: Final Year Project
Language:English
Published: IRC 2020
Subjects:
Q Science (General)
Online Access:http://utpedia.utp.edu.my/21835/1/23834_Nur%20Atiqah%20Mohammed%20Shariff.pdf
http://utpedia.utp.edu.my/21835/
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spelling my-utp-utpedia.218352021-09-24T09:55:27Z http://utpedia.utp.edu.my/21835/ GENETIC ALGORITHM WITH DEEP NEURAL NETWORK SURROGATE FOR THE OPTIMIZATION OF ELECTROMAGNETIC STRUCTURE MOHAMMED SHARIFF, NUR ATIQAH Q Science (General) In this current technological era, engineering design process encounters with multiple complexity as it depends on computer simulation to ensure accurate model before the model is actually constructed. This leads to the problems where the engineers have to feed their simulation with new inputs and parameters to get the best solution. Specifically, in electromagnetic field, bidirectional scattering distribution function of a diffraction grating is computed using MEEP simulation and requires numerous numbers of parameters. This paper will report on an initial study of the usage of Genetic Algorithm (GA) merged with Deep Neural Network based surrogate model to optimize simulation for electromagnetic structure. The behavior of Genetic Algorithm (GA) where it generates and evolves the parameters towards a high-quality solution gives an advantage in obtaining ideal combination of parameters to fit in with the simulation. The GA will be assisted with the convergence of deep neural network to increase the performance by reducing the computational time. IRC 2020-01 Final Year Project NonPeerReviewed application/pdf en http://utpedia.utp.edu.my/21835/1/23834_Nur%20Atiqah%20Mohammed%20Shariff.pdf MOHAMMED SHARIFF, NUR ATIQAH (2020) GENETIC ALGORITHM WITH DEEP NEURAL NETWORK SURROGATE FOR THE OPTIMIZATION OF ELECTROMAGNETIC STRUCTURE. IRC, Universiti Teknologi PETRONAS. (Submitted)
institution Universiti Teknologi Petronas
building UTP Resource Centre
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Petronas
content_source UTP Electronic and Digitized Intellectual Asset
url_provider http://utpedia.utp.edu.my/
language English
topic Q Science (General)
spellingShingle Q Science (General)
MOHAMMED SHARIFF, NUR ATIQAH
GENETIC ALGORITHM WITH DEEP NEURAL NETWORK SURROGATE FOR THE OPTIMIZATION OF ELECTROMAGNETIC STRUCTURE
description In this current technological era, engineering design process encounters with multiple complexity as it depends on computer simulation to ensure accurate model before the model is actually constructed. This leads to the problems where the engineers have to feed their simulation with new inputs and parameters to get the best solution. Specifically, in electromagnetic field, bidirectional scattering distribution function of a diffraction grating is computed using MEEP simulation and requires numerous numbers of parameters. This paper will report on an initial study of the usage of Genetic Algorithm (GA) merged with Deep Neural Network based surrogate model to optimize simulation for electromagnetic structure. The behavior of Genetic Algorithm (GA) where it generates and evolves the parameters towards a high-quality solution gives an advantage in obtaining ideal combination of parameters to fit in with the simulation. The GA will be assisted with the convergence of deep neural network to increase the performance by reducing the computational time.
format Final Year Project
author MOHAMMED SHARIFF, NUR ATIQAH
author_facet MOHAMMED SHARIFF, NUR ATIQAH
author_sort MOHAMMED SHARIFF, NUR ATIQAH
title GENETIC ALGORITHM WITH DEEP NEURAL NETWORK SURROGATE FOR THE OPTIMIZATION OF ELECTROMAGNETIC STRUCTURE
title_short GENETIC ALGORITHM WITH DEEP NEURAL NETWORK SURROGATE FOR THE OPTIMIZATION OF ELECTROMAGNETIC STRUCTURE
title_full GENETIC ALGORITHM WITH DEEP NEURAL NETWORK SURROGATE FOR THE OPTIMIZATION OF ELECTROMAGNETIC STRUCTURE
title_fullStr GENETIC ALGORITHM WITH DEEP NEURAL NETWORK SURROGATE FOR THE OPTIMIZATION OF ELECTROMAGNETIC STRUCTURE
title_full_unstemmed GENETIC ALGORITHM WITH DEEP NEURAL NETWORK SURROGATE FOR THE OPTIMIZATION OF ELECTROMAGNETIC STRUCTURE
title_sort genetic algorithm with deep neural network surrogate for the optimization of electromagnetic structure
publisher IRC
publishDate 2020
url http://utpedia.utp.edu.my/21835/1/23834_Nur%20Atiqah%20Mohammed%20Shariff.pdf
http://utpedia.utp.edu.my/21835/
_version_ 1739832919243882496
score 13.188404

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