Machine Learning as a universal tool for quantitative investigations of phase transitions

Cinzia Giannetti, Biagio Lucini^*, Davide Vadacchino

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

Access to Document

@article{90e15568fc5542c3b4352a517665d032,

title = "Machine Learning as a universal tool for quantitative investigations of phase transitions",

author = "Cinzia Giannetti and Biagio Lucini and Davide Vadacchino",

year = "2019",

month = jul,

doi = "10.1016/j.nuclphysb.2019.114639",

language = "English",

volume = "944",

pages = "114639--114639",

journal = "Nuclear Physics B",

issn = "0550-3213",

publisher = "Elsevier B.V.",

number = "0",

}

TY - JOUR

T1 - Machine Learning as a universal tool for quantitative investigations of phase transitions

AU - Giannetti, Cinzia

AU - Lucini, Biagio

AU - Vadacchino, Davide

PY - 2019/7

Y1 - 2019/7

U2 - 10.1016/j.nuclphysb.2019.114639

DO - 10.1016/j.nuclphysb.2019.114639

M3 - Article

SN - 0550-3213

VL - 944

SP - 114639

EP - 114639

JO - Nuclear Physics B

JF - Nuclear Physics B

IS - 0

ER -