Supervised, semi-supervised, and unsupervised learning of the Domany-Kinzel model

Kui Tuo; Wei Li; Shengfeng Deng; Yueying Zhu

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arXiv · 2309.13990

Supervised, semi-supervised, and unsupervised learning of the Domany-Kinzel model

Kui Tuo·Wei Li·Shengfeng Deng·Yueying Zhu

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Abstract

The Domany Kinzel (DK) model encompasses several types of non-equilibrium phase transitions, depending on the selected parameters. We apply supervised, semi-supervised, and unsupervised learning methods to studying the phase transitions and critical behaviors of the (1 + 1)-dimensional DK model. The supervised and the semi-supervised learning methods permit the estimations of the critical points, the spatial and temporal correlation exponents, concerning labelled and unlabelled DK configurations, respectively. Furthermore, we also predict the critical points by employing principal component analysis (PCA) and autoencoder. The PCA and autoencoder can produce results in good agreement with simulated particle number density.

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Kui Tuo, Wei Li, Shengfeng Deng, Yueying Zhu. 2023-09-25. Supervised, semi-supervised, and unsupervised learning of the Domany-Kinzel model. https://arxiv.org/abs/2309.13990

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