相关论文: Deep learning the holographic black hole with char…

We construct a neural network to learn the RN-AdS black hole metric based on the data of optical conductivity by holography. The linear perturbative equation for the Maxwell field is rewritten in terms of the optical conductivity such that…

Based on AdS/CFT correspondence, we build a deep neural network to learn black hole metrics from the complex frequency-dependent shear viscosity. The network architecture provides a discretized representation of the holographic…

According to AdS/DL (Anti de Sitter/ Deep Learning) correspondence given by \cite{Has}, in this paper with a data-driven approach and leveraging holography principle we have designed an artificial neural network architecture to produce…

We present a deep neural network representation of the AdS/CFT correspondence, and demonstrate the emergence of the bulk metric function via the learning process for given data sets of response in boundary quantum field theories. The…

We propose a deep learning method to build an AdS/QCD model from the data of hadron spectra. A major problem of generic AdS/QCD models is that a large ambiguity is allowed for the bulk gravity metric with which QCD observables are…

In this report we describe quantum Reissner-Nordstr\"om (RN) black-holes interacting with a complex scalar field. Our analysis is characterized by solving a Wheeler-DeWitt equation in the proximity of an apparent horizon of the RN…

We investigate the bulk reconstruction of AdS black hole spacetime emergent from quantum entanglement within a machine learning framework. Utilizing neural ordinary differential equations alongside Monte-Carlo integration, we develop a…

We consider the $\mathrm{AdS}_3/\mathrm{CFT}_2$ description of Reissner-Nordstr{\o}m black holes by studying their uplifted counterparts in five dimensions. Assuming a natural size of the extra dimension, the near horizon geometries for the…

We study a multiply warped product manifold associated with the Reissner-Nordstrom-AdS metric to investigate the physical properties inside the black hole event horizons. Our results include various limiting geometries of the RN,…

We study the quantum behaviour of Reissner-Nordstr\"om (RN) black-holes interacting with a complex scalar field. A Maxwell field is also present. Our analysis is based on M. Pollock's method and is characterized by solving a Wheeler-DeWitt…

We discuss the shadow cast by the charged Reissner-Nordstr\"om (RN) AdS black hole. With the help of Killing equation and Hamilton-Jacobi equation, we calculate the geodesic equations for null particle. With the help of geodesics of null…

In the framework of black hole spectroscopy, we extend the results obtained for a charged black hole in an asymptotically flat spacetime to the scenario with non vanishing negative cosmological constant. In particular, exploiting…

We provide a deep Boltzmann machine (DBM) for the AdS/CFT correspondence. Under the philosophy that the bulk spacetime is a neural network, we give a dictionary between those, and obtain a restricted DBM as a discretized bulk scalar field…

We present a new solution in Einstein's theory of relativity, found through the use of the symmetries of the Ernst equations and in particular the Harrison and Ehlers transformations. The new metric represents a Reissner-Nordstr\"om black…

In this article, we study the superradiance of charged scalar fields on the sub-extremal Reissner-Nordstrom metric, a mechanism by which such fields can extract energy from a static spherically symmetric charged black hole. A geometrical…

We consider deep inelastic scattering (DIS) on a large nucleus described as an extremal RN-AdS black hole using the holographic principle. Using the R-current correlators we determine the structure functions as a function Bjorken-x, and map…

In this paper, we employ a general relativistic formalism and develop new theoretical tools that allow us to analytically express the mass and electric charge of the Reissner-Nordstr\"{o}m black hole as well as its distance to a distant…

Deep metric learning, which learns discriminative features to process image clustering and retrieval tasks, has attracted extensive attention in recent years. A number of deep metric learning methods, which ensure that similar examples are…

We study black hole formation in the head-on collision of ultrarelativistic charges. The metric of charged particles is obtained by boosting the Reissner-Nordstr\"om spacetime to the speed of light. Using the slice at the instant of…

We propose a novel deep learning tool in order to study the evolution of dark energy models. The aim is to combine two architectures: the Recurrent Neural Networks (RNN) and the Bayesian Neural Networks (BNN), we named this full network as…