Aspects of holographic entanglement using physics-informed-neural-networks

Anirudh Deb, Yaman Sanghavi
9/29/2025
hep-thcs.LGphysics.comp-ph

No confident code implementation found for this paper.

Abstract

We implement physics-informed-neural-networks (PINNs) to compute holographic entanglement entropy and entanglement wedge cross section. This technique allows us to compute these quantities for arbitrary shapes of the subregions in any asymptotically AdS metric. We test our computations against some known results and further demonstrate the utility of PINNs in examples, where it is not straightforward to perform such computations.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. Any repos shown below are weak matches — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

AdS/Deep-Learning made easy II: neural network-based approaches to holography and inverse problems

82% match2025

Exploring the holographic entropy cone via reinforcement learning

78% match2026

Learning the Inverse Ryu--Takayanagi Formula with Transformers

78% match2025

Quantized neural network for complex hologram generation

77% match2024

Learning Chern Numbers of Topological Insulators with Gauge Equivariant Neural Networks

76% match1 repo42025

What Makes Data Suitable for a Locally Connected Neural Network? A Necessary and Sufficient Condition Based on Quantum Entanglement

76% match2026

Cite this paper

@article{deb2025aspects,
  title  = {Aspects of holographic entanglement using physics-informed-neural-networks},
  author = {Anirudh Deb and Yaman Sanghavi},
  year   = {2025},
  eprint = {2509.25311},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2509.25311v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2509.25311.svg)](https://codeofpaper.com/paper/2509.25311)

Discussion