Deep Learning and AdS/CFT

Understand

We present a deep neural network representation of the AdS/CFT correspondence, and demonstrate the emergence of the bulk metric function via the learning process for given data sets of response in boundary quantum field theories.

• The emergent radial direction of the bulk is identified with the depth of the layers, and the network itself is interpreted as a bulk geometry.
• Our network provides a data-driven holographic modeling of strongly coupled systems.
• With a scalar $\phi^4$ theory with unknown mass and coupling, in unknown curved spacetime with a black hole horizon, we demonstrate our deep learning (DL) framework can determine them which fit given response data.