Generalization capabilities of neural networks in lattice applications

TL;DR

This paper demonstrates that translationally equivariant neural networks outperform non-equivariant ones in lattice field theory tasks, showing better generalization across physical parameters and lattice sizes.

Contribution

It systematically compares equivariant and non-equivariant neural networks, highlighting the advantages of incorporating symmetries in lattice applications.

Findings

Equivariant networks outperform non-equivariant ones in regression and classification tasks.

Equivariant architectures generalize better to unseen physical parameters.

Performance gains extend across different lattice sizes.

Abstract

In recent years, the use of machine learning has become increasingly popular in the context of lattice field theories. An essential element of such theories is represented by symmetries, whose inclusion in the neural network properties can lead to high reward in terms of performance and generalizability. A fundamental symmetry that usually characterizes physical systems on a lattice with periodic boundary conditions is equivariance under spacetime translations. Here we investigate the advantages of adopting translationally equivariant neural networks in favor of non-equivariant ones. The system we consider is a complex scalar field with quartic interaction on a two-dimensional lattice in the flux representation, on which the networks carry out various regression and classification tasks. Promising equivariant and non-equivariant architectures are identified with a systematic search. We demonstrate that in most of these tasks our best equivariant architectures can perform and generalize significantly better than their non-equivariant counterparts, which applies not only to physical parameters beyond those represented in the training set, but also to different lattice sizes.