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| Format: | Preprint |
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2026
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| Online Access: | https://arxiv.org/abs/2604.05970 |
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| _version_ | 1866911573904195584 |
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| author | Filev, Veselin G. |
| author_facet | Filev, Veselin G. |
| contents | We apply artificial neural networks to the holographic inverse problem, reconstructing bulk geometry from boundary entanglement entropy by using the Ryu--Takayanagi area functional as a differentiable loss. Validated on the AdS-Schwarzschild background, this approach recovers the blackening factor to 1.7% accuracy. For finite-density backgrounds like the Gubser--Rocha model, we demonstrate that strip entanglement entropy determines only the spatial metric. We resolve this exact one-function degeneracy by incorporating holographic Wilson loop data, which couples to the timelike metric. We present a semi-analytical inversion combining Bilson's and Hashimoto's formulas, alongside a general three-network variational method minimizing the combined area and Nambu--Goto actions. The neural network achieves sub-0.2% accuracy for both metric functions without closed-form derivative relations, establishing a flexible framework for integrating multiple holographic observables. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_05970 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Holographic entanglement entropy, Wilson loops, and neural networks Filev, Veselin G. High Energy Physics - Theory We apply artificial neural networks to the holographic inverse problem, reconstructing bulk geometry from boundary entanglement entropy by using the Ryu--Takayanagi area functional as a differentiable loss. Validated on the AdS-Schwarzschild background, this approach recovers the blackening factor to 1.7% accuracy. For finite-density backgrounds like the Gubser--Rocha model, we demonstrate that strip entanglement entropy determines only the spatial metric. We resolve this exact one-function degeneracy by incorporating holographic Wilson loop data, which couples to the timelike metric. We present a semi-analytical inversion combining Bilson's and Hashimoto's formulas, alongside a general three-network variational method minimizing the combined area and Nambu--Goto actions. The neural network achieves sub-0.2% accuracy for both metric functions without closed-form derivative relations, establishing a flexible framework for integrating multiple holographic observables. |
| title | Holographic entanglement entropy, Wilson loops, and neural networks |
| topic | High Energy Physics - Theory |
| url | https://arxiv.org/abs/2604.05970 |