arXiv:2509.25311hep-thcs.LG2025-09被引 5
用物理启发神经网络计算全息纠缠熵，支持任意形状子区域。
Aspects of holographic entanglement using physics-informed-neural-networks
- 用物理约束的神经网络求解全息纠缠问题
- 可在任意AdS度规下计算纠缠熵与楔交叉截面
- 适合研究复杂几何下的量子引力现象
我们采用物理启发神经网络（PINNs）计算全息纠缠熵和纠缠楔交叉截面。该方法可针对任意形状的子区域，在任意渐近AdS度规下进行计算。我们通过已知结果验证了计算的准确性，并在一些传统方法难以处理的例子中展示了PINNs的实用价值。
原文摘要 · Abstract (English)
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.
全息纠缠神经网络量子引力
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