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Lecture notes in computer science

Supervised and Transfer Learning for Phase Transition Research

Vladislav Chertenkov, Щур Лев Николаевич

Published Jan 1, 2025• 0 citations
Publisher
DOI
10.1007/978-3-031-78459-0_31

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Neural networks in machine learning are highly capable of classifying data and recognizing graphics, and have been widely used in statistical physics, particularly in phase transitions. In this paper, we review recent developments in machine learning algorithms in the application of primary phase transition models. We first introduce the background of modern machine learning algorithms and related pioneering work in equilibrium phase transitions. Second, by taking directed percolation as an example of a typical non-equilibrium phase transition model, we present some of the latest research findings of our group. The research involves supervised, unsupervised, and semi-supervised learning for classifying phases, predicting critical points, and measuring critical exponents. Thereafter, we elaborate on related work on machine learning algorithms in phase transition research in quantum many-body theory, soft matter physics, and high-energy physics. Lastly, corresponding discussions and prospects are presented.
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. By means of case studies, we show the potential of this deep learning technique to detect phases and their transitions in frustrated spin systems, using dense 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 which 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.