Phase space sampling and operator confidence with generative adversarial networks

Kyle Mills; Isaac Tamblyn

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arXiv · 1710.08053

Phase space sampling and operator confidence with generative adversarial networks

Kyle Mills·Isaac Tamblyn

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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.

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Kyle Mills, Isaac Tamblyn. 2017-10-23. Phase space sampling and operator confidence with generative adversarial networks. https://arxiv.org/abs/1710.08053

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