Data: Learning Lattice Quantum Field Theories with Equivariant Continuous Flows
DOI10.5281/zenodo.7547918Zenodo7547918MaRDI QIDQ6710911
Dataset published at Zenodo repository.
Author name not available (Why is that?)
Publication date: 18 January 2023
Copyright license: No records found.
Network parameters of continuous normalizing flows trained for the\(\varphi^4\) theory. Corresponding article:Learning Lattice Quantum Field Theories with Equivariant Continuous Flows [2207.00283] 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\(\varphi^4\) 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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