[2509.25311] Aspects of holographic entanglement using physics-informed-neural-networks

High Energy Physics - Theory arXiv:2509.25311 (hep-th) [Submitted on 29 Sep 2025 (v1), last revised 28 Mar 2026 (this version, v2)] Title:Aspects of holographic entanglement using physics-informed-neural-networks Authors:Anirudh Deb, Yaman Sanghavi View a PDF of the paper titled Aspects of holographic entanglement using physics-informed-neural-networks, by Anirudh Deb and 1 other authors View PDF HTML (experimental) 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. Comments: Subjects: High Energy Physics - Theory (hep-th); Machine Learning (cs.LG); Computational Physics (physics.comp-ph) Report number: YITP-SB-2025-18 Cite as: arXiv:2509.25311 [hep-th]   (or arXiv:2509.25311v2 [hep-th] for this version)   https://doi.org/10.48550/arXiv.2509.25311 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Anirudh Deb [view email] [v1] Mon, 29 Sep 2025 18:00:00 UTC (5,164 KB) [v2] Sat, 28 Mar 2026 15:29:47 UTC (5,161 KB) Full-text links: Access Paper: View a PDF of the paper titled Aspects of holographic entanglement using physics-informed-neural-networks, by Anirudh Deb an...