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Revealing the Three-Dimensional Magn...

Zhao, Shihua.

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Revealing the Three-Dimensional Magnetic Texture with Machine Learning Models.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Revealing the Three-Dimensional Magnetic Texture with Machine Learning Models./
Author:	Zhao, Shihua.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2023,
Description:	153 p.
Notes:	Source: Dissertations Abstracts International, Volume: 84-08, Section: B.
Contained By:	Dissertations Abstracts International84-08B.
Subject:	Condensed matter physics. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30248550
ISBN:	9798374405637

Revealing the Three-Dimensional Magnetic Texture with Machine Learning Models.
Zhao, Shihua.

Revealing the Three-Dimensional Magnetic Texture with Machine Learning Models. - Ann Arbor : ProQuest Dissertations & Theses, 2023 - 153 p.

Source: Dissertations Abstracts International, Volume: 84-08, Section: B.

Thesis (Ph.D.)--City University of New York, 2023.

Revealing three-dimensional (3D) magnetic textures with vector field electron tomography (VFET) is essential in studying novel magnetic materials with topologically protected spin textures potentially being used in the next-generation semiconductor industry. In this dissertation, we use machine learning (ML) models to reconstruct 3D magnetic textures from electron holography (EH) data.We can feed the EH data, a series of two-dimensional (2D) phasemaps, into a neural network (NN) architecture directly or feed the EH data into a conventional VFET and then feed the reconstructed results into a NN. Thus, perceptive NN, either a simple convolutional neural network (CNN) or Unet architecture, is built and used to reconstruct the 3D magnetic texture. We demonstrate that the magnetic vector potential and magnetic induction field can be successfully reconstructed with an end-to-end Unet-based ML model. Also, reconstruction results of conventional VFET can be significantly enhanced with a plug-and-play Unet attached to it. The scaling law for run time versus dataset size is studied. Reconstruction results of EH data with various defects, such as noise, sparsity, misalignment, and missing wedge, are also discussed in the frame of ML models with Unet architecture.Furthermore, a generative model is introduced to reconstruct the magnetization to solve the missing information of scalar potential that EH cannot probe. Integrating the cycle consistency and a forward model from magnetization to EH phasemap, we build a cycle consistency generative adversarial network (cycleGAN) based generative model that gives impressive reconstruction results of magnetization. This cycle consistency with a forward model generative model framework is also a promising solution for other inverse problems with an explicit forward model.

ISBN: 9798374405637Subjects--Topical Terms:

3173567
Condensed matter physics.
Subjects--Index Terms:

3D magnetic reconstruction

Revealing the Three-Dimensional Magnetic Texture with Machine Learning Models.
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245 1 0 $a Revealing the Three-Dimensional Magnetic Texture with Machine Learning Models.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2023
300 $a 153 p.
500 $a Source: Dissertations Abstracts International, Volume: 84-08, Section: B.
500 $a Advisor: Ghaemi, Pouyan;Zang, Jiadong.
502 $a Thesis (Ph.D.)--City University of New York, 2023.
520 $a Revealing three-dimensional (3D) magnetic textures with vector field electron tomography (VFET) is essential in studying novel magnetic materials with topologically protected spin textures potentially being used in the next-generation semiconductor industry. In this dissertation, we use machine learning (ML) models to reconstruct 3D magnetic textures from electron holography (EH) data.We can feed the EH data, a series of two-dimensional (2D) phasemaps, into a neural network (NN) architecture directly or feed the EH data into a conventional VFET and then feed the reconstructed results into a NN. Thus, perceptive NN, either a simple convolutional neural network (CNN) or Unet architecture, is built and used to reconstruct the 3D magnetic texture. We demonstrate that the magnetic vector potential and magnetic induction field can be successfully reconstructed with an end-to-end Unet-based ML model. Also, reconstruction results of conventional VFET can be significantly enhanced with a plug-and-play Unet attached to it. The scaling law for run time versus dataset size is studied. Reconstruction results of EH data with various defects, such as noise, sparsity, misalignment, and missing wedge, are also discussed in the frame of ML models with Unet architecture.Furthermore, a generative model is introduced to reconstruct the magnetization to solve the missing information of scalar potential that EH cannot probe. Integrating the cycle consistency and a forward model from magnetization to EH phasemap, we build a cycle consistency generative adversarial network (cycleGAN) based generative model that gives impressive reconstruction results of magnetization. This cycle consistency with a forward model generative model framework is also a promising solution for other inverse problems with an explicit forward model.
590 $a School code: 0046.
650 4 $a Condensed matter physics. $3 3173567
650 4 $a Materials science. $3 543314
653 $a 3D magnetic reconstruction
653 $a CycleGAN
653 $a Electron holography
653 $a Machine learning
653 $a Skyrmion
653 $a Unet
690 $a 0611
690 $a 0800
690 $a 0794
710 2 $a City University of New York. $b Physics. $3 1025716
773 0 $t Dissertations Abstracts International $g 84-08B.
790 $a 0046
791 $a Ph.D.
792 $a 2023
793 $a English
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30248550