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

DOI10.1016/j.nuclphysb.2019.114639zbMath1430.82006arXiv1812.06726OpenAlexW2904646909MaRDI QIDQ2295509

Cinzia Giannetti, Davide Vadacchino, Biagio Lucini

Publication date: 13 February 2020

Published in: Nuclear Physics. B (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1812.06726

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Critical exponents in context of PDEs (35B33) Phase transitions (general) in equilibrium statistical mechanics (82B26) Lattice systems (Ising, dimer, Potts, etc.) and systems on graphs arising in equilibrium statistical mechanics (82B20) Critical phenomena in equilibrium statistical mechanics (82B27) Statistical thermodynamics (82B30)

Related Items (2)

Emergence of a finite-size-scaling function in the supervised learning of the Ising phase transition ⋮ Stochastic normalizing flows as non-equilibrium transformations

Uses Software

• Scikit

Cites Work

• Support-vector networks
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• Editorial. Introduction to the proceedings of the Workshop on topology and topological groups (WTTG2017) held at the African Institute for Mathematical Sciences (AIMS) in Muizenberg (South Africa) on the 6th and 7th of December 2017
• Invariant kernel functions for pattern analysis and machine learning
• High-Dimensional Data Classification
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