Learning Lattice Quantum Field Theories with Equivariant Continuous Flows

• URL: http://arxiv.org/abs/2207.00283v3
• Date: Wed, 20 Dec 2023 14:15:49 GMT
• Title: Learning Lattice Quantum Field Theories with Equivariant Continuous Flows
• Authors: Mathis Gerdes, Pim de Haan, Corrado Rainone, Roberto Bondesan, Miranda C. N. Cheng
• Abstract summary: We propose a novel machine learning method for sampling from the high-dimensional probability distributions of Lattice Field Theories. We test our model on the $phi4$ theory, showing that it systematically outperforms previously proposed flow-based methods in sampling efficiency.
• Score: 10.124564216461858
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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