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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…
physics.space-ph2021★ 25 cited
Machine learning for predicting the Bz magnetic field component from upstream in situ observations of solar coronal mass ejections
Martin A. Reiss, Christian Möstl, Rachel L. Bailey +6
Predicting the Bz magnetic field embedded within ICMEs, also known as the Bz problem, is a key challenge in space weather forecasting. We study the hypothesis that upstream in situ…