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

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