Machine learning the 2D percolation model

Abstract

We use deep-learning strategies to study the 2D percolation model on a square lattice. We employ standard image recognition tools with a multi-layered convolutional neural network. We test how well these strategies can characterise densities and correlation lengths of percolation states and whether the essential role of the percolating cluster is recognised.

6 pages, 14 figures, XXXII IUPAP Conference on Computational Physics

References in corpus (3)

• PyTorch: An Imperative Style, High-Performance Deep Learning Library
• Drawing Phase Diagrams of Random Quantum Systems by Deep Learning the Wave Functions
• Machine Learning Percolation Model

Cited by in corpus (3)

• Machine learning of phases and structures for model systems in physics
• Phase determination with and without deep learning
• Large-Angle Convergent-Beam Electron Diffraction Patterns via Conditional Generative Adversarial Networks