A Curriculum-Training-Based Strategy for Distributing Collocation Points during Physics-Informed Neural Network Training

no code implementations • 21 Nov 2022 • Marcus Münzer, Chris Bard

Physics-informed Neural Networks (PINNs) often have, in their loss functions, terms based on physical equations and derivatives.

Prediction of soft proton intensities in the near-Earth space using machine learning

1 code implementation • 11 May 2021 • Elena A. Kronberg, Tanveer Hannan, Jens Huthmacher, Marcus Münzer, Florian Peste, Ziyang Zhou, Max Berrendorf, Evgeniy Faerman, Fabio Gastaldello, Simona Ghizzardi, Philippe Escoubet, Stein Haaland, Artem Smirnov, Nithin Sivadas, Robert C. Allen, Andrea Tiengo, Raluca Ilie

The average correlation between the observed and predicted data is about 56%, which is reasonable in light of the complex dynamics of fast-moving energetic protons in the magnetosphere.

Astronomy BIG-bench Machine Learning