# Deep learning the holographic black hole with charge

## TL;DR

This paper employs deep learning to reconstruct the Reissner-Nordström black hole metric in AdS space by training neural networks on boundary data and analyzing the effects of training parameters.

## Contribution

It introduces a neural network approach to learn charged black hole metrics from boundary data, bridging holography and machine learning.

## Key findings

- Successful reconstruction of RN black hole metric
- Analysis of training parameter effects on learning process
- Potential for applying deep learning to other holographic models

## Abstract

We use the deep learning algorithm to learn the Reissner-Nordstr\"om(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.