用Transformer从纠缠熵反推引力几何，实现高精度重建。
Learning the Inverse Ryu--Takayanagi Formula with Transformers
- 用Transformer学习反解瑞-高桥公式，从熵值还原几何函数。
- 在平滑黑洞和无视界背景上重建准确率超90%。
- 适合对全息对偶与神经网络结合感兴趣的学者。
我们采用数据驱动的生成模型研究AdS₃中全息纠缠熵的逆问题。训练数据由随机生成的几何结构及其通过瑞-高桥公式计算出的纠缠熵组成。训练完成后，Transformer能够从全新输入中重构出我们度规假设下的黑色函数。该模型在光滑黑洞几何上实现高精度重建，并可外推至无视界背景。本文详细描述了网络架构与数据生成流程，并量化评估了在f(z)和重构纠缠熵S(ℓ)上的表现。代码与评估脚本可在所提供仓库中获取。
原文摘要 · Abstract (English)
We study the inverse problem of holographic entanglement entropy in AdS$_3$ 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 $S(\ell)$. Code and evaluation scripts are available at the provided repository.
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