Identifying internal patterns in (1+1)-dimensional directed percolation using neural networks
This work addresses the challenge of analyzing complex percolation patterns in statistical physics, though it appears incremental as it applies existing neural network architectures to a specific domain.
The paper tackled the problem of automatically detecting phase transitions and classifying hidden patterns in (1+1)-dimensional directed percolation using a neural network, achieving accurate reproduction of the phase diagram and assignment of phase labels to configurations.
In this paper we present a neural network-based method for the automatic detection of phase transitions and classification of hidden percolation patterns in a (1+1)-dimensional replication process. The proposed network model is based on the combination of CNN, TCN and GRU networks, which are trained directly on raw configurations without any manual feature extraction. The network reproduces the phase diagram and assigns phase labels to configurations. It shows that deep architectures are capable of extracting hierarchical structures from the raw data of numerical experiments.