Learning the Inverse Ryu--Takayanagi Formula with Transformers
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Abstract (arXiv)
We study the inverse problem of holographic entanglement entropy in AdS3 using a data-driven generative model. Training data consist of randomly generated geometries and their holographic entanglement entropies using the Ryu--Takayanagi formula. After training, the Transformer reconstructs the blackening function within our metric ansatz from previously unseen inputs. The Transformer achieves accurate reconstructions on smooth black hole geometries and extrapolates to horizonless backgrounds. We describe the architecture and data generation process, and we quantify accuracy on both f(z) and the reconstructed S(ℓ). Code and evaluation scripts are available at the provided repository.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 9 of its 48 references that are in the map. First posted on arXiv 9 Nov 2025.
Builds on
9 of 48 references.
References · 9
- 2017Attention Is All You Needcited by 60,361
- 2017Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equationscited by 373
- 2017Physics Informed Deep Learning (Part II): Data-driven Discovery of Nonlinear Partial Differential Equationscited by 161
- 2019A unified sparse optimization framework to learn parsimonious physics-informed models from datacited by 49
- 2024Gravitational Duals from Equations of Statecited by 10
- 2020AdS/Deep-Learning made easy: simple examplescited by 9
- 2024Machine-learning emergent spacetime from linear response in future tabletop quantum gravity experimentscited by 5
- 2025Aspects of holographic entanglement using physics-informed-neural-networkscited by 5
- 2024Phase Diagram from Nonlinear Interaction between Superconducting Order and Density: Toward Data-Based Holographic Superconductorcited by 3
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Citing papers per year: 2025: 1; 2026: 1.
2 papers.