most citedDeep reinforcement learning for complex evaluation of one-loop diagrams in quantum field theory

7 citations · 9 across the 2 of their papers we have counts for

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hep-ph2021★ 2 cited

A machine learning pipeline for autonomous numerical analytic continuation of Dyson-Schwinger equations

Andreas Windisch, Thomas Gallien, Christopher Schwarzlmueller

Dyson-Schwinger equations (DSEs) are a non-perturbative way to express n-point functions in quantum field theory. Working in Euclidean space and in Landau gauge, for example, one c…

hep-ph2019★ 7 cited

Deep reinforcement learning for complex evaluation of one-loop diagrams in quantum field theory

Andreas Windisch, Thomas Gallien, Christopher Schwarzlmueller

In this paper we present a novel technique based on deep reinforcement learning that allows for numerical analytic continuation of integrals that are often encountered in one-loop…