RT Journal Article
T1 Learning a local symmetry with neural networks
A1 Decelle, A.
A1 Martín Mayor, Víctor
A1 Seoane, B.
AB We explore the capacity of neural networks to detect a symmetry with complex local and non-local patterns: the gauge symmetry Z(2). This symmetry is present in physical problems from topological transitions to quantum chromodynamics, and controls the computational hardness of instances of spin-glasses. Here, we show how to design a neural network, and a dataset, able to learn this symmetry and to find compressed latent representations of the gauge orbits. Our method pays special attention to system-wrapping loops, the so-called Polyakov loops, known to be particularly relevant for computational complexity.
PB American Physical Society
SN 2470-0045
YR 2019
FD 2019-11-06
LK https://hdl.handle.net/20.500.14352/5980
UL https://hdl.handle.net/20.500.14352/5980
LA eng
NO ©2019 American Physical Society.We thank L. A. Fernandez for encouraging discussions and Marco Baity-Jesi for his careful reading of the manuscript. This work was partially supported by Ministerio de Economia, Industria y Competitividad (MINECO) (Spain) and by EU's FEDER program through Grants No. FIS2015-65078-C2-1-P and No. PGC2018-094684-B-C21 and by the LabEx CALSIMLAB (public Grant No. ANR-11-LABX-0037-01 constituting a part of the "Investissements d'Avenir" program - reference No. ANR-11-IDEX-0004-02).
NO Ministerio de Economía y Competitividad (MINECO)/FEDER
NO LabEx CALSIMLAB
DS Docta Complutense
RD 16 may 2024