Machine-learning physics from unphysics: Finding deconfinement temperature in lattice Yang-Mills theories from outside the scaling window

no code implementations • 23 Sep 2020 • D. L. Boyda, M. N. Chernodub, N. V. Gerasimeniuk, V. A. Goy, S. D. Liubimov, A. V. Molochkov

We study the machine learning techniques applied to the lattice gauge theory's critical behavior, particularly to the confinement/deconfinement phase transition in the SU(2) and SU(3) gauge theories.

BIG-bench Machine Learning valid

Topological defects and confinement with machine learning: the case of monopoles in compact electrodynamics

no code implementations • 16 Jun 2020 • M. N. Chernodub, Harold Erbin, V. A. Goy, A. V. Molochkov

We investigate the advantages of machine learning techniques to recognize the dynamics of topological objects in quantum field theories.

BIG-bench Machine Learning

Casimir effect with machine learning

no code implementations • 18 Nov 2019 • M. N. Chernodub, Harold Erbin, I. V. Grishmanovskii, V. A. Goy, A. V. Molochkov

Vacuum fluctuations of quantum fields between physical objects depend on the shapes, positions, and internal composition of the latter.

BIG-bench Machine Learning