Aspects of holographic entanglement using physics-informed-neural-networks
High Energy Physics - Theory
2026-03-31 v2 Machine Learning
Computational Physics
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.
Keywords
Cite
@article{arxiv.2509.25311,
title = {Aspects of holographic entanglement using physics-informed-neural-networks},
author = {Anirudh Deb and Yaman Sanghavi},
journal= {arXiv preprint arXiv:2509.25311},
year = {2026}
}
Comments
19 pages, 14 figures. v2: minor corrections, references added, revised figure captions for clarity