Application of RMT-RNN improved decomposition onto defected system
Name
1027215949-MIT.pdf
Description
Full printable version
Size
7.03 MB
Format
Adobe PDF
Checksum (MD5)
735de3edc1fbc9222704a40be60864a1
Author(s)
Xie, Wanqin, Ph. D. Massachusetts Institute of Technology
Advisor(s)
Roy E. Welsch.
Alternative Title
Application of Random Matrix Theory coupled with Neural Networks improved decomposition onto defected system
Date Issued
2017
Publisher
Massachusetts Institute of Technology
Abstract
This thesis is about the study and application of a stochastic optimization algorithm - Random Matrix Theory coupled with Neural Networks (RMT-RNN) to large static systems with relatively large disorder in mesoscopic systems. It is a new algorithm that can quickly decompose random matrices with real eigenvalues for further study of physical properties, such as transmission probability, conductivity and so on. As a major topic of Random Matrix Theory (RMT), free convolution has managed to approximate the distribution of eigenvalues in the Anderson Model. RMT has proven to work well when looking for the transport properties in slightly defect system. Systems with larger disorder require to take in account of the changes in eigenvectors as well. Hence, combined with parallelizable Neural Network (RNN), RMT-RNN turns out to be a great approach for eigenpair approximation for systems with large defects.
Description
Thesis: Ph. D. in Physical Chemistry, Massachusetts Institute of Technology, Department of Chemistry, 2017.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 72-77).
Subjects
Chemistry.
MIT Department
Massachusetts Institute of Technology. Department of Chemistry
Terms of Use
MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.
Persistent DSpace Link
