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    •   BracU IR
    • School of Data and Sciences (SDS)
    • Department of Computer Science and Engineering (CSE)
    • Thesis & Report, BSc (Computer Science and Engineering)
    • View Item
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    Classi cation of magnetic configurations using machine learning algorithms

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    16301001, 16201037, 17301181_CSE.pdf (7.595Mb)
    Date
    2019-08
    Publisher
    Brac University
    Author
    Bokul, Saffat
    Abdus Shukur, Samiha Sabrin Md
    Ahmed, Saquib
    Metadata
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    URI
    http://hdl.handle.net/10361/12825
    Abstract
    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.
    Keywords
    Convolutional Neural Network; Support vector machine; Principle component analysis; Skyrmion; Ferromagnetic; Spin-spira; Antiskyrmion; Anti-ferromagnetic; Topological
     
    LC Subject Headings
    Machine learning; Computer algorithms; Machine learning--Mathematical models
     
    Description
    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).
    Department
    Department of Computer Science and Engineering, Brac University
    Type
    Thesis
    Collections
    • Thesis & Report, BSc (Computer Science and Engineering)

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