Data: Learning Lattice Quantum Field Theories with Equivariant Continuous Flows

<p>Network parameters of continuous normalizing flows trained for the&nbsp;<span class="math-tex">\(\varphi^4\)</span> theory.</p> <p>Corresponding article:&nbsp;Learning Lattice Quantum Field Theories with Equivariant Continuous Flows [<a href="https://arxiv.org/abs/2207.00283">2207.00283</a>]</p> <p>Abstract:&nbsp;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&nbsp;<span class="math-tex">\(\varphi^4\)</span> 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.</p>