Aspects of holographic entanglement using physics-informed-neural-networks
This work addresses computational challenges in theoretical physics, specifically in holography, by applying an existing machine learning method to a new domain, making it incremental.
The paper tackled the problem of computing holographic entanglement entropy and entanglement wedge cross section for arbitrary subregion shapes in asymptotically AdS metrics by implementing physics-informed neural networks (PINNs), demonstrating utility in cases where traditional computations are challenging.
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.