Please use this identifier to cite or link to this item:
http://hdl.handle.net/2445/202717
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | Rios Huguet, Arnau | - |
dc.contributor.advisor | Rozalén Sarmiento, Javier | - |
dc.contributor.author | Azzam, Amir | - |
dc.date.accessioned | 2023-10-11T12:57:00Z | - |
dc.date.available | 2023-10-11T12:57:00Z | - |
dc.date.issued | 2023-07 | - |
dc.identifier.uri | http://hdl.handle.net/2445/202717 | - |
dc.description | Màster Oficial de Ciència i Tecnologia Quàntiques / Quantum Science and Technology, Facultat de Física, Universitat de Barcelona. Curs: 2022-2023. Tutors: A. Rios, J. Rozalén | ca |
dc.description.abstract | We study the ground-state properties of fully-polarized, trapped, one-dimensional fermions interacting through a Gaussian potential using a variational wavefunction represented by an antisymmetric artificial neural network. We optimize the network parameters by minimizing the energy of a four-particle system with machine learning techniques. We introduce several methods to enhance the efficiency, accuracy, and scalability of the artificial neural network approach. We use Early Stopping to terminate the training when the energy converges. We apply Transfer Learning to pre-train the network with different interactions or particle numbers to reduce the memory cost and improve the performance. We employ a re-weighting scheme to speed up the Metropolis- Hastings sampling for estimating the energy expectation value. We show that, in particular, this re-weighting scheme can speed up the training time by up to a factor of 10. | ca |
dc.format.extent | 29 p. | - |
dc.format.mimetype | application/pdf | - |
dc.language.iso | eng | ca |
dc.rights | cc-by-nc-nd (c) Azan, 2023 | - |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ | * |
dc.source | Màster Oficial - Ciència i Tecnologia Quàntiques / Quantum Science and Technology | - |
dc.subject.classification | Xarxa neuronal artificial | - |
dc.subject.classification | Estats quàntics de xarxa neuronal | - |
dc.subject.classification | Aprenentatge per transferència | - |
dc.subject.classification | Treballs de fi de màster | - |
dc.subject.other | Artificial neural network | - |
dc.subject.other | Neural network quantum states | - |
dc.subject.other | Transfer learning | - |
dc.subject.other | Master's thesis | - |
dc.title | Transfer Learning For Many-Body Systems | eng |
dc.type | info:eu-repo/semantics/masterThesis | ca |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | ca |
Appears in Collections: | Màster Oficial - Ciència i Tecnologia Quàntiques / Quantum Science and Technology |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
TFM_Amir_Azzam.pdf | 3.45 MB | Adobe PDF | View/Open |
This item is licensed under a
Creative Commons License