Classi cation of magnetic configurations using machine learning algorithms

Classi cation of magnetic configurations using machine learning algorithms

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2019.

Detaylı Bibliyografya
Asıl Yazarlar: Bokul, Saffat, Abdus Shukur, Samiha Sabrin Md, Ahmed, Saquib
Diğer Yazarlar: Alam, Md. Ashraful
Materyal Türü: Tez
Dil:English
Baskı/Yayın Bilgisi: Brac University 2019
Konular:
Convolutional Neural Network
Support vector machine
Principle component analysis
Skyrmion
Ferromagnetic
Spin-spira
Antiskyrmion
Anti-ferromagnetic
Topological
Machine learning
Computer algorithms
Machine learning > Mathematical models
Online Erişim:http://hdl.handle.net/10361/12825
id 10361-12825
record_format dspace
spelling 10361-128252022-01-26T10:15:53Z Classi cation of magnetic configurations using machine learning algorithms Bokul, Saffat Abdus Shukur, Samiha Sabrin Md Ahmed, Saquib Alam, Md. Ashraful Department of Computer Science and Engineering, Brac University Convolutional Neural Network Support vector machine Principle component analysis Skyrmion Ferromagnetic Spin-spira Antiskyrmion Anti-ferromagnetic Topological Machine learning Computer algorithms Machine learning--Mathematical models This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2019. Cataloged from PDF version of thesis. Includes bibliographical references (pages 50-53). Machine learning is used to carry out e cient studies and analyses in the eld of condensed matter physics. We propose comprehensive machine learning approaches that would classify between magnetic structures. We propose models that are trained on data that has been generated on 3D lattices of Heisenberg model using the physical properties of respective magnetic structures. Models are designed based on three types of classi cations, rst classi cation is done between topologically-protected structures, second on non-topologically-protected structures, thirdly on all structures collectively. To achieve this, convolutional neural network (CNN) and support vector machine (SVM) with principle component analysis (PCA) algorithms have been used. We then make a comparative analysis and nd the most optimal solution. The results show that CNN provides the highest accuracy in the classi cation of topological and non-topological magnetic con gurations. Saffat Bokul Samiha Sabrin Md Abdus Shukur Saquib Ahmed B. Computer Science 2019-11-04T04:01:41Z 2019-11-04T04:01:41Z 2019 2019-08 Thesis ID 16301001 ID 16201037 ID 17301181 http://hdl.handle.net/10361/12825 en Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 53 pages application/pdf Brac University
institution Brac University
collection Institutional Repository
language English
topic Convolutional Neural Network
Support vector machine
Principle component analysis
Skyrmion
Ferromagnetic
Spin-spira
Antiskyrmion
Anti-ferromagnetic
Topological
Machine learning
Computer algorithms
Machine learning--Mathematical models
spellingShingle Convolutional Neural Network
Support vector machine
Principle component analysis
Skyrmion
Ferromagnetic
Spin-spira
Antiskyrmion
Anti-ferromagnetic
Topological
Machine learning
Computer algorithms
Machine learning--Mathematical models
Bokul, Saffat
Abdus Shukur, Samiha Sabrin Md
Ahmed, Saquib
Classi cation of magnetic configurations using machine learning algorithms
description This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2019.
author2 Alam, Md. Ashraful
author_facet Alam, Md. Ashraful
Bokul, Saffat
Abdus Shukur, Samiha Sabrin Md
Ahmed, Saquib
format Thesis
author Bokul, Saffat
Abdus Shukur, Samiha Sabrin Md
Ahmed, Saquib
author_sort Bokul, Saffat
title Classi cation of magnetic configurations using machine learning algorithms
title_short Classi cation of magnetic configurations using machine learning algorithms
title_full Classi cation of magnetic configurations using machine learning algorithms
title_fullStr Classi cation of magnetic configurations using machine learning algorithms
title_full_unstemmed Classi cation of magnetic configurations using machine learning algorithms
title_sort classi cation of magnetic configurations using machine learning algorithms
publisher Brac University
publishDate 2019
url http://hdl.handle.net/10361/12825
work_keys_str_mv AT bokulsaffat classicationofmagneticconfigurationsusingmachinelearningalgorithms
AT abdusshukursamihasabrinmd classicationofmagneticconfigurationsusingmachinelearningalgorithms
AT ahmedsaquib classicationofmagneticconfigurationsusingmachinelearningalgorithms
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