.png){kind=icon}
返回
Machine learning Post-Minkowskian integrals
.png){kind=icon}
OA
AI
DOI：10.1007/JHEP07(2023)181.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
We study a neural network framework for the numerical evaluation of Feynman loop integrals that are fundamental building blocks for perturbative computations of physical observables in gauge and gravity theories. We show that such a machine learning approach improves the convergence of the Monte Carlo algorithm for high-precision evaluation of multi-dimensional integrals compared to traditional algorithms. In particular, we use a neural network to improve the importance sampling. For a set of representative integrals appearing in the computation of the conservative dynamics for a compact binary system in General Relativity, we perform a quantitative comparison between the Monte Carlo integrators VEGAS and i-flow, an integrator based on neural network sampling.
Keyword:
Scattering Amplitudes
Classical Theories of Gravity
Effective Field Theories
期刊
IF:
5.5
论文数:
3.9W
被引数:
13.7W
机构
引用论文
Modelling exchange kinetics of copper at the water-aquatic moss (Fontinalis antipyretica) interface: Influence of water cationic composition (Ca, Mg, Na and pH).png)
Chemosphere
OAAI
ChemosphereIF0
OAAI分享收藏分享收藏
查看更多内容