Phase space sampling and operator confidence with generative adversarial networks

TL;DR

This paper shows how GANs can generate Ising model configurations across phase space and use discriminator confidence as an anomaly detection tool for operator estimation.

Contribution

It introduces a method where GAN discriminators serve as anomaly detectors and confidence estimators for physical operator predictions.

Findings

GANs can generate diverse phase space configurations.

Discriminator confidence correlates with prediction accuracy.

Method enables anomaly detection in physical models.

Abstract

We demonstrate that a generative adversarial network can be trained to produce Ising model configurations in distinct regions of phase space. In training a generative adversarial network, the discriminator neural network becomes very good a discerning examples from the training set and examples from the testing set. We demonstrate that this ability can be used as an anomaly detector, producing estimations of operator values along with a confidence in the prediction.