Deep learning the holographic black hole with charge

Abstract

We use the deep learning algorithm to learn the Reissner-Nordström(RN) black hole metric by building a deep neural network. Plenty of data is made in boundary of AdS and we propagate it to the black hole horizon through AdS metric and equation of motion(e.o.m). We label this data according to the values near the horizon, and together with initial data constitute a data set. Then we construct corresponding deep neural network and train it with the data set to obtain the Reissner-Nordstrom(RN) black hole metric. Finally, we discuss the effects of learning rate, batch-size and initialization on the training process.

14 pages, 8 figures

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• Gauge Theory Correlators from Non-Critical String Theory
• The World as a Hologram
• Entanglement Renormalization and Holography
• Deep Learning and AdS/CFT
• Holography as deep learning

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