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 …

This article proposes a four-dimensional brain mapping method, which can represent the
continuous process of a person's fatigue state in the form of image frames in a space-time …

We propose a normalized deep neural network (norm-DNN) for computing ground states of
Bose-Einstein condensation (BEC) via the minimization of the Gross-Pitaevskii energy …

Abstract Quantum vortices in Bose–Einstein condensates (BECs) are essential phenomena
in condensed matter physics, and precisely locating their positions, especially the vortex …

Abstract Machine Learning methods are emerging as faster and efficient alternatives to
numerical simulation techniques. The field of Scientific Computing has started adopting …

Abstract Machine learning has been a powerful tool to study various models in many fields,
which requires plenty of data to ensure accuracy. It is a challenge to reduce the dependence …

A 别物质的相变是物理学W 究中个重要问题. & 文采用了# 混BCD 方案的E 积F 经GH 算法来A
别两分量I 色r J 因KLM 聚(BEC) 中量子相变点, 通过计算F 经GHNi 的O 确率, 得到W …

Deep learning, accounting for the use of an elaborate neural network, has recently been
developed as an efficient and powerful tool to solve diverse problems in physics and other …

This study focuses on numerically investigating the emergence of vortices in the ground
state ofthe rotating Bose-Einstein Condensates (BECs) within a Gross-Pitaevskii framework …

A sub-class of variational quantum algorithms (VQAs), the quantum convolutional neural
network (QCNN), has emerged as an efficient quantum error correction (QEC) algorithm and …