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Detecting the Many-Body Localization Transition with Machine Learning Techniques
KTH, School of Engineering Sciences (SCI), Physics.
2018 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Place, publisher, year, edition, pages
2018.
Series
TRITA-SCI-GRU ; 2018:207
National Category
Physical Sciences
Identifiers
URN: urn:nbn:se:kth:diva-231327OAI: oai:DiVA.org:kth-231327DiVA, id: diva2:1224039
Available from: 2018-06-26 Created: 2018-06-26 Last updated: 2018-06-26Bibliographically approved

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File name FULLTEXT01.pdfFile size 3056 kBChecksum SHA-512
26aeebd83e2713a4f45f4aec550909a88e151bc311220bffd5278424dc1360c431c9ba4ac39e6355897387aba43062ff2f0efd29cea22990b00b4dfe84c45775
Type fulltextMimetype application/pdf

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CiteExportLink to record
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  • apa
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