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Machine learning the 2D percolation model
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Bayo, Djénabou, Honecker, Andreas and Roemer, Rudolf A. (2022) Machine learning the 2D percolation model. Journal of Physics: Conference Series, 2207 (1). 012057. doi:10.1088/1742-6596/2207/1/012057 ISSN 1742-6596.
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Official URL: https://doi.org/10.1088/1742-6596/2207/1/012057
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.
Item Type: | Journal Article | ||||
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Subjects: | Q Science > Q Science (General) Q Science > QA Mathematics |
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Physics | ||||
SWORD Depositor: | Library Publications Router | ||||
Library of Congress Subject Headings (LCSH): | Machine learning, Deep learning (Machine learning), Lattice theory, Neural networks (Computer science) -- Computer simulation | ||||
Journal or Publication Title: | Journal of Physics: Conference Series | ||||
Publisher: | Institute of Physics Publishing Ltd. | ||||
ISSN: | 1742-6596 | ||||
Official Date: | 1 March 2022 | ||||
Dates: |
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Volume: | 2207 | ||||
Number: | 1 | ||||
Article Number: | 012057 | ||||
DOI: | 10.1088/1742-6596/2207/1/012057 | ||||
Status: | Peer Reviewed | ||||
Publication Status: | Published | ||||
Access rights to Published version: | Open Access (Creative Commons) | ||||
Date of first compliant deposit: | 23 May 2022 | ||||
Date of first compliant Open Access: | 24 May 2022 |
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