深度学习：计算核物理的工具
Deep Learning: A Tool for Computational Nuclear Physics
摘要：
近年来，人工神经网络在核物理、高能物理、生物学、化学、气象学等科学领域都有了成功的应用。核理论的一个主要目标是从强相互作用的基础理论量子色动力学（QCD）来预测原子核结构和核反应。随着高性能计算（HPC）系统的发展，一些从头计算方法，如无核壳模型（NCSM），已经被开发出来计算原子核的性质。然而，要精确求解原子核的性质，人们面临着巨大的理论和计算挑战。本文提出了一种前馈人工神经网络方法，用于预测原子核的性质，如基态能量和基态点质子均方根（rms）半径的基础上NCSM结果在计算可访问的基空间。所设计的人工神经网络是足够的，以产生这两个非常不同的可观的结果在6Li从从头算NCSM结果在小的基础空间，满足理论物理条件：独立的基础空间参数的限制，极大的矩阵。我们还提供了从人工神经网络的结果与建立的方法估计的结果在无限矩阵极限的比较。
英文摘要：
In recent years, several successful applications of the Artificial Neural Networks (ANNs) have emerged in nuclear physics and high-energy physics, as well as in biology, chemistry, meteorology, and other fields of science. A major goal of nuclear theory is to predict nuclear structure and nuclear reactions from the underlying theory of the strong interactions, Quantum Chromodynamics (QCD). With access to powerful High Performance Computing (HPC) systems, several ab initio approaches, such as the No-Core Shell Model (NCSM), have been developed to calculate the properties of atomic nuclei. However, to accurately solve for the properties of atomic nuclei, one faces immense theoretical and computational challenges. The present study proposes a feed-forward ANN method for predicting the properties of atomic nuclei like ground state energy and ground state point proton root-mean-square (rms) radius based on NCSM results in computationally accessible basis spaces. The designed ANNs are sufficient to produce results for these two very different observables in 6Li from the ab initio NCSM results in small basis spaces that satisfy the theoretical physics condition: independence of basis space parameters in the limit of extremely large matrices. We also provide comparisons of the results from ANNs with established methods of estimating the results in the infinite matrix limit.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
