Percolation detection using convolutional deep neural networks

Esteban Iriarte, Joaquín Peralta, Claudia Loyola, Sergio Davis

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


This work describes the percolation phenomena in different structures through deep neural networks and previously calculated statistical data of percolation. Despite being relatively simple and easy to calculate at small scales, the percolation process is computationally time-consuming at large scales; here, a significant computation is necessary to determine if a cluster percolates or not. We propose to train deep neural networks on small systems and scale to large systems. Our results show a reasonable accuracy rate on recognition of images, particularly on fully convolutional neural networks for the continuum case, a recent improvement on classical convolutional neural networks, improving the recognition of percolation phenomena, portability, and scalability.

Original languageEnglish
Title of host publicationIWoSP 2021 - International Workshop on Statistical Physics
EditorsBoris Atenas, Francisco A. Calderon, Sergio Curilef, Yuvineza Gomez-Leyton, Diego Gonzalez
PublisherAmerican Institute of Physics Inc.
ISBN (Electronic)9780735444829
Publication statusPublished - 15 May 2023
Event2021 International Workshop on Statistical Physics, IWoSP 2021 - Antofagasta, Chile
Duration: 1 Dec 20213 Dec 2021

Publication series

NameAIP Conference Proceedings
ISSN (Print)0094-243X
ISSN (Electronic)1551-7616


Conference2021 International Workshop on Statistical Physics, IWoSP 2021

ASJC Scopus subject areas

  • General Physics and Astronomy


Dive into the research topics of 'Percolation detection using convolutional deep neural networks'. Together they form a unique fingerprint.

Cite this