mlsanityData to 14 Oct 2026

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

arXiv 2509.25311 ↗First posted 29 Sep 2025hep-th cs.LG physics.comp-phcited by 5

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Abstract (arXiv)

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.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 7 of its 51 references that are in the map. First posted on arXiv 29 Sep 2025.

Builds on

7 of 51 references.

References · 7

  1. 2014Deep Learning in Neural Networks: An Overviewcited by 770
  2. 2022Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and What's nextcited by 608
  3. 2017Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equationscited by 373
  4. 2018A high-bias, low-variance introduction to Machine Learning for physicistscited by 108
  5. 2024Numerical analysis of physics-informed neural networks and related models in physics-informed machine learningcited by 39
  6. 2024Flow-Based Sampling for Entanglement Entropy and the Machine Learning of Defectscited by 13
  7. 2025Physics-informed neural network solves minimal surfaces in curved spacetimecited by 6

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 3.

5 papers.

  1. 2026A Machine Learning Approach to the Nirenberg Problemcited by 5
  2. 2025Learning the Inverse Ryu--Takayanagi Formula with Transformerscited by 2
  3. 2025AdS/Deep-Learning made easy II: neural network-based approaches to holography and inverse problemscited by 2
  4. 2026Minimal surfaces, Knots, and Neural Networkscited by 2
  5. 2026Machine Learning Meets High-Energy Nuclear Physics: From Pattern Recognition to Physics-Integrated Discoverycited by 0