Neural Net Analysis of Phase Transitions in Geometrically Frustrated Classical Models. Generation of Training Samples

Student: Gorbunova Valentina

Supervisor: Evgeny Burovskiy

Faculty: HSE Tikhonov Moscow Institute of Electronics and Mathematics (MIEM HSE)

Educational Programme: Applied Mathematics (Bachelor)

Final Grade: 8

Year of Graduation: 2021

We describe the development of the Monte-Carlo simulation of geometrically frustrated classical Ising models. The general task is to explore the ability of Neural Networks to classify phases and identify critical behavior in different Ising models of the same universality class, which are geometrically frustrated models. The task was divided into two parts and in this paper we solve the first one — generation of training samples. The simulation is based on the Metropolis algorithm.

Full text (added May 27, 2021)

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