Exploring neural network training strategies to determine phase transitions in frustrated magnetic models

The transfer learning of a neural network is one of its most outstanding aspects and has given supervised learning with neural networks a prominent place in data science. Here we explore this feature in the context of strongly interacting many-body systems. Through case studies, we test the potentia...

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Detalles Bibliográficos
Autores principales:	Corte, Inés Raquel, Acevedo, Santiago Daniel, Arlego, Marcelo José Fabián, Lamas, Carlos Alberto
Formato:	Articulo
Lenguaje:	Inglés
Publicado:	2021
Materias:	Ingeniería Física Frustrated magnetism Machine learning Ising model Honeycomb lattice Square lattice Neural networks
Acceso en línea:	http://sedici.unlp.edu.ar/handle/10915/131979
Aporte de:	SEDICI (UNLP) de Universidad Nacional de La Plata

id	I19-R120-10915-131979
record_format	dspace
institution	Universidad Nacional de La Plata
institution_str	I-19
repository_str	R-120
collection	SEDICI (UNLP)
language	Inglés
topic	Ingeniería Física Frustrated magnetism Machine learning Ising model Honeycomb lattice Square lattice Neural networks
spellingShingle	Ingeniería Física Frustrated magnetism Machine learning Ising model Honeycomb lattice Square lattice Neural networks Corte, Inés Raquel Acevedo, Santiago Daniel Arlego, Marcelo José Fabián Lamas, Carlos Alberto Exploring neural network training strategies to determine phase transitions in frustrated magnetic models
topic_facet	Ingeniería Física Frustrated magnetism Machine learning Ising model Honeycomb lattice Square lattice Neural networks
description	The transfer learning of a neural network is one of its most outstanding aspects and has given supervised learning with neural networks a prominent place in data science. Here we explore this feature in the context of strongly interacting many-body systems. Through case studies, we test the potential of this deep learning technique to detect phases and their transitions in frustrated spin systems, using fully-connected and convolutional neural networks. In addition, we explore a recently-introduced technique, which is at the middle point of supervised and unsupervised learning. It consists in evaluating the performance of a neural network that has been deliberately “confused” during its training. To properly demonstrate the capability of the “confusion” and transfer learning techniques, we apply them to a paradigmatic model of frustrated magnetism in two dimensions, to determine its phase diagram and compare it with high-performance Monte Carlo simulations.
format	Articulo Articulo
author	Corte, Inés Raquel Acevedo, Santiago Daniel Arlego, Marcelo José Fabián Lamas, Carlos Alberto
author_facet	Corte, Inés Raquel Acevedo, Santiago Daniel Arlego, Marcelo José Fabián Lamas, Carlos Alberto
author_sort	Corte, Inés Raquel
title	Exploring neural network training strategies to determine phase transitions in frustrated magnetic models
title_short	Exploring neural network training strategies to determine phase transitions in frustrated magnetic models
title_full	Exploring neural network training strategies to determine phase transitions in frustrated magnetic models
title_fullStr	Exploring neural network training strategies to determine phase transitions in frustrated magnetic models
title_full_unstemmed	Exploring neural network training strategies to determine phase transitions in frustrated magnetic models
title_sort	exploring neural network training strategies to determine phase transitions in frustrated magnetic models
publishDate	2021
url	http://sedici.unlp.edu.ar/handle/10915/131979
work_keys_str_mv	AT corteinesraquel exploringneuralnetworktrainingstrategiestodeterminephasetransitionsinfrustratedmagneticmodels AT acevedosantiagodaniel exploringneuralnetworktrainingstrategiestodeterminephasetransitionsinfrustratedmagneticmodels AT arlegomarcelojosefabian exploringneuralnetworktrainingstrategiestodeterminephasetransitionsinfrustratedmagneticmodels AT lamascarlosalberto exploringneuralnetworktrainingstrategiestodeterminephasetransitionsinfrustratedmagneticmodels
bdutipo_str	Repositorios
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Exploring neural network training strategies to determine phase transitions in frustrated magnetic models

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