{"@context":{"@vocab":"https://cir.nii.ac.jp/schema/1.0/","rdfs":"http://www.w3.org/2000/01/rdf-schema#","dc":"http://purl.org/dc/elements/1.1/","dcterms":"http://purl.org/dc/terms/","foaf":"http://xmlns.com/foaf/0.1/","prism":"http://prismstandard.org/namespaces/basic/2.0/","cinii":"http://ci.nii.ac.jp/ns/1.0/","datacite":"https://schema.datacite.org/meta/kernel-4/","ndl":"http://ndl.go.jp/dcndl/terms/","jpcoar":"https://github.com/JPCOAR/schema/blob/master/2.0/"},"@id":"https://cir.nii.ac.jp/crid/1363670320791591680.json","@type":"Article","productIdentifier":[{"identifier":{"@type":"DOI","@value":"10.1103/physreve.96.022140"}},{"identifier":{"@type":"URI","@value":"https://link.aps.org/article/10.1103/PhysRevE.96.022140"}},{"identifier":{"@type":"URI","@value":"http://harvest.aps.org/v2/journals/articles/10.1103/PhysRevE.96.022140/fulltext"}}],"dc:title":[{"@value":"Unsupervised learning of phase transitions: From principal component analysis to variational autoencoders"}],"creator":[{"@id":"https://cir.nii.ac.jp/crid/1383670320791591680","@type":"Researcher","foaf:name":[{"@value":"Sebastian J. Wetzel"}]}],"publication":{"publicationIdentifier":[{"@type":"PISSN","@value":"24700045"},{"@type":"EISSN","@value":"24700053"}],"prism:publicationName":[{"@value":"Physical Review E"}],"dc:publisher":[{"@value":"American Physical Society (APS)"}],"prism:publicationDate":"2017-08-18","prism:volume":"96","prism:number":"2","prism:startingPage":"022140"},"reviewed":"false","dc:rights":["https://link.aps.org/licenses/aps-default-license"],"url":[{"@id":"https://link.aps.org/article/10.1103/PhysRevE.96.022140"},{"@id":"http://harvest.aps.org/v2/journals/articles/10.1103/PhysRevE.96.022140/fulltext"}],"createdAt":"2017-08-18","modifiedAt":"2019-10-02","relatedProduct":[{"@id":"https://cir.nii.ac.jp/crid/1360004235026071552","@type":"Article","resourceType":"学術雑誌論文(journal article)","relationType":["isReferencedBy"],"jpcoar:relatedTitle":[{"@value":"Deep learning and the \n<mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" display=\"inline\"><mml:mrow><mml:mi>AdS</mml:mi><mml:mo>/</mml:mo><mml:mi>CFT</mml:mi></mml:mrow></mml:math>\n correspondence"}]},{"@id":"https://cir.nii.ac.jp/crid/1360009142616691968","@type":"Article","resourceType":"学術雑誌論文(journal article)","relationType":["isReferencedBy"],"jpcoar:relatedTitle":[{"@value":"Topological quantum phase transitions retrieved through unsupervised machine learning"}]},{"@id":"https://cir.nii.ac.jp/crid/1360302864802846464","@type":"Article","resourceType":"学術雑誌論文(journal article)","relationType":["isReferencedBy"],"jpcoar:relatedTitle":[{"@value":"Machine-learning detection of the Berezinskii-Kosterlitz-Thouless transition and the second-order phase transition in XXZ models"}]},{"@id":"https://cir.nii.ac.jp/crid/1360565167770620032","@type":"Article","resourceType":"学術雑誌論文(journal article)","relationType":["isReferencedBy"],"jpcoar:relatedTitle":[{"@value":"Restricted Boltzmann machine learning for solving strongly correlated quantum systems"}]},{"@id":"https://cir.nii.ac.jp/crid/1360565167787838592","@type":"Article","resourceType":"学術雑誌論文(journal article)","relationType":["isReferencedBy"],"jpcoar:relatedTitle":[{"@value":"Scale-invariant feature extraction of neural network and renormalization group flow"}]},{"@id":"https://cir.nii.ac.jp/crid/1360576118767989504","@type":"Article","resourceType":"学術雑誌論文(journal article)","relationType":["isReferencedBy"],"jpcoar:relatedTitle":[{"@value":"Topological persistence machine of phase transitions"}]},{"@id":"https://cir.nii.ac.jp/crid/1360581935562015360","@type":"Article","relationType":["isReferencedBy"],"jpcoar:relatedTitle":[{"@value":"Weakly-supervised Learning of Schrödinger Equation"}]},{"@id":"https://cir.nii.ac.jp/crid/1360848659965682688","@type":"Article","resourceType":"学術雑誌論文(journal article)","relationType":["isReferencedBy"],"jpcoar:relatedTitle":[{"@value":"Transforming generalized Ising models into Boltzmann machines"}]},{"@id":"https://cir.nii.ac.jp/crid/1360857593664782464","@type":"Article","resourceType":"学術雑誌論文(journal article)","relationType":["isReferencedBy"],"jpcoar:relatedTitle":[{"@value":"Machine learning detection of Berezinskii-Kosterlitz-Thouless transitions in \n<mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"><mml:mi>q</mml:mi></mml:math>\n-state clock models"}]},{"@id":"https://cir.nii.ac.jp/crid/1360861295469436160","@type":"Article","resourceType":"学術雑誌論文(journal article)","relationType":["isReferencedBy"],"jpcoar:relatedTitle":[{"@value":"Feature Space of XRD Patterns Constructed by an Autoencoder"}]},{"@id":"https://cir.nii.ac.jp/crid/1520022941213226368","@type":"Article","resourceType":"学術雑誌論文(journal article)","relationType":["isReferencedBy"],"jpcoar:relatedTitle":[{"@value":"Machine-Learning Detection of the Berezinskii-Kosterlitz-Thouless Transitions"}]},{"@id":"https://cir.nii.ac.jp/crid/1520292182332191488","@type":"Article","resourceType":"学術雑誌論文(journal article)","relationType":["isReferencedBy"],"jpcoar:relatedTitle":[{"@value":"Randomized-Gauge Test for Machine Learning of Ising Model Order Parameter"}]},{"@id":"https://cir.nii.ac.jp/crid/1521136280935209344","@type":"Article","resourceType":"学術雑誌論文(journal article)","relationType":["isReferencedBy"],"jpcoar:relatedTitle":[{"@value":"Drawing Phase Diagrams of Random Quantum Systems by Deep Learning the Wave Functions"}]},{"@id":"https://cir.nii.ac.jp/crid/1521699231065782272","@type":"Article","resourceType":"学術雑誌論文(journal article)","relationType":["isReferencedBy"],"jpcoar:relatedTitle":[{"@value":"Machine Learning of Mirror Skin Effects in the Presence of Disorder"}]},{"@id":"https://cir.nii.ac.jp/crid/2050307417125011840","@type":"Article","resourceType":"学術雑誌論文(journal article)","relationType":["isReferencedBy"],"jpcoar:relatedTitle":[{"@value":"Phase transition encoded in neural network"}]},{"@id":"https://cir.nii.ac.jp/crid/2051151842058087808","@type":"Article","resourceType":"学術雑誌論文(journal article)","relationType":["isReferencedBy"],"jpcoar:relatedTitle":[{"@value":"Can a CNN trained on the Ising model detect the phase transition of the q-state Potts model?"}]}],"dataSourceIdentifier":[{"@type":"CROSSREF","@value":"10.1103/physreve.96.022140"},{"@type":"CROSSREF","@value":"10.1103/physrevd.98.046019_references_DOI_NXLR5v4xnfuA8gpQ77tHz6XhdbK"},{"@type":"CROSSREF","@value":"10.7566/jpsj.90.053703_references_DOI_NXLR5v4xnfuA8gpQ77tHz6XhdbK"},{"@type":"CROSSREF","@value":"10.1103/physrevb.102.134213_references_DOI_NXLR5v4xnfuA8gpQ77tHz6XhdbK"},{"@type":"CROSSREF","@value":"10.7566/jpsj.91.044001_references_DOI_NXLR5v4xnfuA8gpQ77tHz6XhdbK"},{"@type":"CROSSREF","@value":"10.1103/physrevb.107.134420_references_DOI_NXLR5v4xnfuA8gpQ77tHz6XhdbK"},{"@type":"CROSSREF","@value":"10.7566/jpsj.94.031003_references_DOI_NXLR5v4xnfuA8gpQ77tHz6XhdbK"},{"@type":"CROSSREF","@value":"10.1103/physrevb.96.205152_references_DOI_NXLR5v4xnfuA8gpQ77tHz6XhdbK"},{"@type":"CROSSREF","@value":"10.1103/physreve.97.053304_references_DOI_NXLR5v4xnfuA8gpQ77tHz6XhdbK"},{"@type":"CROSSREF","@value":"10.7566/jpsj.89.022001_references_DOI_NXLR5v4xnfuA8gpQ77tHz6XhdbK"},{"@type":"CROSSREF","@value":"10.1103/physreve.103.052127_references_DOI_NXLR5v4xnfuA8gpQ77tHz6XhdbK"},{"@type":"CROSSREF","@value":"10.1002/adts.202200613_references_DOI_NXLR5v4xnfuA8gpQ77tHz6XhdbK"},{"@type":"CROSSREF","@value":"10.7566/jpsj.93.064002_references_DOI_NXLR5v4xnfuA8gpQ77tHz6XhdbK"},{"@type":"CROSSREF","@value":"10.1103/physreve.99.032113_references_DOI_NXLR5v4xnfuA8gpQ77tHz6XhdbK"},{"@type":"CROSSREF","@value":"10.1093/ptep/ptab057_references_DOI_NXLR5v4xnfuA8gpQ77tHz6XhdbK"},{"@type":"CROSSREF","@value":"10.1103/physrevb.104.075114_references_DOI_NXLR5v4xnfuA8gpQ77tHz6XhdbK"},{"@type":"CROSSREF","@value":"10.1093/ptep/ptz082_references_DOI_NXLR5v4xnfuA8gpQ77tHz6XhdbK"}]}