9 citations · 10 across the 2 of their papers we have counts for
2 papers
cs.LG2022★ 1 cited
A Curriculum-Training-Based Strategy for Distributing Collocation Points during Physics-Informed Neural Network Training
Marcus Münzer, Chris Bard
Physics-informed Neural Networks (PINNs) often have, in their loss functions, terms based on physical equations and derivatives. In order to evaluate these terms, the output soluti…
physics.space-ph2021★ 9 cited
Prediction of soft proton intensities in the near-Earth space using machine learning
Elena A. Kronberg, Tanveer Hannan, Jens Huthmacher +14
The spatial distribution of energetic protons contributes towards the understanding of magnetospheric dynamics. Based upon 17 years of the Cluster/RAPID observations, we have deriv…