Learning lattice quantum field theories with equivariant continuous flows
Mathis Gerdes, Pim de Haan, Corrado Rainone, Roberto Bondesan, Miranda C. N. Cheng
SciPost Phys. 15, 238 (2023) · published 13 December 2023
- doi: 10.21468/SciPostPhys.15.6.238
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Abstract
We propose a novel machine learning method for sampling from the high-dimensional probability distributions of Lattice Field Theories, which is based on a single neural ODE layer and incorporates the full symmetries of the problem. We test our model on the $\phi^4$ theory, showing that it systematically outperforms previously proposed flow-based methods in sampling efficiency, and the improvement is especially pronounced for larger lattices. Furthermore, we demonstrate that our model can learn a continuous family of theories at once, and the results of learning can be transferred to larger lattices. Such generalizations further accentuate the advantages of machine learning methods.
Cited by 15
Authors / Affiliations: mappings to Contributors and Organizations
See all Organizations.- 1 Mathis Gerdes,
- 2 3 Pim de Haan,
- 3 Corrado Rainone,
- 3 Roberto Bondesan,
- 1 2 4 Miranda C. N. Cheng