Data augmentation for disruption prediction via robust surrogate models
Katharina Rath(Max Planck Institute for Plasma Physics), Christopher G. Albert(Graz University of Technology), Cristina Rea(Plasma Technology (United States)), Andrew Maris(Plasma Technology (United States)), Bernd Bischl(Munich Center for Machine Learning), R. Granetz(Plasma Technology (United States)), David Rügamer(Munich Center for Machine Learning), U. von Toussaint(Max Planck Institute for Plasma Physics)
Cited by 7
Related Papers
Hyperparameter optimization: Foundations, algorithms, best practices, and open challenges
|Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery|2023|777
MANTA: a negative-triangularity NASEM-compliant fusion pilot plant
|Plasma Physics and Controlled Fusion|2024|26
The Impact of Disruptions on the Economics of a Tokamak Power Plant
|Fusion Science & Technology|2023|17
Autoregressive Transformers for Disruption Prediction in Nuclear Fusion Plasmas
|arXiv (Cornell University)|2023|5
Chaos in the quantum Duffing oscillator in the semiclassical regime under parametrized dissipation
|Physical review. E|2021|4