Exploring neural network training strategies to determine phase transitions in frustrated magnetic models
Abstract
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.
Keywords
Cite
@article{arxiv.2009.00661,
title = {Exploring neural network training strategies to determine phase transitions in frustrated magnetic models},
author = {I. Corte and S. Acevedo and M. Arlego and C. A. Lamas},
journal= {arXiv preprint arXiv:2009.00661},
year = {2021}
}