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

2025/09/29 by Deb, Anirudh, Sanghavi, Yaman · 2 citations
#Computational Physics (physics.comp-ph) #FOS: Computer and information sciences #FOS: Physical sciences #High Energy Physics - Theory (hep-th) #Machine Learning (cs.LG)

paper · doi:10.48550/arxiv.2509.25311

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.

Citations

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