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

Deep Neural Networks achieve state-of-the-art results in many different problem settings by exploiting vast amounts of training data. However, collecting, storing and - in the case of supervised learning - labelling the data is expensive…

In this pilot study, we investigate the use of a deep learning (DL) model to temporally evolve the dynamics of gas accreting onto a black hole in the form of a radiatively inefficient accretion flow (RIAF). We have trained a machine to…

Deep Learning (DL) has shown potential in accelerating Magnetic Resonance Image acquisition and reconstruction. Nevertheless, there is a dearth of tailored methods to guarantee that the reconstruction of small features is achieved with high…

We discuss the Reissner-Nordstrom-de Sitter black holes in the context of dS/CFT correspondence by using static and planar coordinates. The boundary stress tensor and the mass of the solutions are computed. Also, we investigate how the RG…

We study the dual CFT description of the $d+1$-dimensional Reissner-Nordstr\"om-Anti de Sitter (RN-AdS$_{d+1}$) black hole in the large dimension (large $d$) limit, both for the extremal and nonextremal cases. The central charge of the dual…

This work investigates the ways in which deep learning methods can benefit from random projection (RP), a classic linear dimensionality reduction method. We focus on two areas where, as we have found, employing RP techniques can improve…

We develop an analytic model that extends classical white hole geometry by incorporating both radiative dynamics and electric charge. Starting from a maximal analytic extension of the Schwarzschild white hole via Kruskal Szekeres…

Deep learning uses neural networks which are parameterised by their weights. The neural networks are usually trained by tuning the weights to directly minimise a given loss function. In this paper we propose to re-parameterise the weights…

Inspired by Loop Quantum Gravity (LQG), we investigate the Reissner-Nordstr\"om (RN) black hole (BH) solution coupled with a cloud of strings in an anti-de Sitter (AdS) background, surrounded by a quintessence-like fluid. We begin by…

We present a Gaussian kernel loss function and training algorithm for convolutional neural networks that can be directly applied to both distance metric learning and image classification problems. Our method treats all training features…

The low-energy scattering of charged fermions by extremal magnetic Reissner-Nordstrom black holes is analyzed in the large-$N$ and $S$-wave approximations. It is shown that (in these approximations) information is carried into a causally…

Generative deep learning methods built upon Convolutional Neural Networks (CNNs) provide a great tool for predicting non-linear structure in cosmology. In this work we predict high resolution dark matter halos from large scale, low…

Compared with traditional seismic noise attenuation algorithms that depend on signal models and their corresponding prior assumptions, removing noise with a deep neural network is trained based on a large training set, where the inputs are…

In this paper, we consider a numerical homogenization of the poroelasticity problem with stochastic properties. The proposed method based on the construction of the deep neural network (DNN) for fast calculation of the effective properties…

Deep learning is an effective approach to solving image recognition problems. People draw intuitive conclusions from trading charts; this study uses the characteristics of deep learning to train computers in imitating this kind of intuition…

We study the geometry inside the event horizon of perturbed D dimensional Reissner-Nordstrom-(A)dS type black holes showing that, similarly to the four dimensional case, mass inflation also occurs for D>4. First, using the homogeneous…

We construct hairy black hole solutions that merge with the anti-de Sitter (AdS$_4$) Reissner-Nordstr\"om black hole at the onset of superradiance. These hairy black holes have, for a given mass and charge, higher entropy than the…

Quantum many-body problem with exponentially large degrees of freedom can be reduced to a tractable computational form by neural network method \cite{CT}. The power of deep neural network (DNN) based on deep learning is clarified by mapping…

We use borehole resistivity measurements to map the electrical properties of the subsurface and to increase the productivity of a reservoir. When used for geosteering purposes, it becomes essential to invert them in real time. In this work,…

We derive for the first time the form of the spiral null geodesics around the photon sphere of the Reissner-Nordstrom black hole in the de Sitter expanding universe. Moreover, we obtain the principal parameter we need for deriving,…