{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,9]],"date-time":"2025-12-09T08:29:10Z","timestamp":1765268950922,"version":"build-2065373602"},"reference-count":46,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2023,12,12]],"date-time":"2023-12-12T00:00:00Z","timestamp":1702339200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Direcci\u00f3n de Investigaci\u00f3n y Postgrado, Universidad Finis Terrae"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>The restricted Boltzmann machine (RBM) is a generative neural network that can learn in an unsupervised way. This machine has been proven to help understand complex systems, using its ability to generate samples of the system with the same observed distribution. In this work, an Ising system is simulated, creating configurations via Monte Carlo sampling and then using them to train RBMs at different temperatures. Then, 1. the ability of the machine to reconstruct system configurations and 2. its ability to be used as a detector of configurations at specific temperatures are evaluated. The results indicate that the RBM reconstructs configurations following a distribution similar to the original one, but only when the system is in a disordered phase. In an ordered phase, the RBM faces levels of irreproducibility of the configurations in the presence of bimodality, even when the physical observables agree with the theoretical ones. On the other hand, independent of the phase of the system, the information embodied in the neural network weights is sufficient to discriminate whether the configurations come from a given temperature well. The learned representations of the RBM can discriminate system configurations at different temperatures, promising interesting applications in real systems that could help recognize crossover phenomena.<\/jats:p>","DOI":"10.3390\/e25121649","type":"journal-article","created":{"date-parts":[[2023,12,12]],"date-time":"2023-12-12T09:15:33Z","timestamp":1702372533000},"page":"1649","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["The Capabilities of Boltzmann Machines to Detect and Reconstruct Ising System\u2019s Configurations from a Given Temperature"],"prefix":"10.3390","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1362-2776","authenticated-orcid":false,"given":"Mauricio A.","family":"Valle","sequence":"first","affiliation":[{"name":"Facultad de Econom\u00eda y Negocios, Universidad Finis Terrae, Santiago 7501015, Chile"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,12,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1126\/science.1127647","article-title":"Reducing the dimensionality of data with neural networks","volume":"313","author":"Hinton","year":"2006","journal-title":"Science"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1671","DOI":"10.1162\/neco_a_01210","article-title":"Learning compositional representations of interacting systems with restricted boltzmann machines: Comparative study of lattice proteins","volume":"31","author":"Tubiana","year":"2019","journal-title":"Neural Comput."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1016\/j.bspc.2018.05.022","article-title":"Compressed sensing ECG using restricted Boltzmann machines","volume":"45","author":"Polania","year":"2018","journal-title":"Biomed. 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