Conveners
ADV/1 Advances in data science, probabilistic methods and machine learning: Session 8
- Geert Verdoolaege (Ghent University)
- Michael Churchill
Superconducting magnets play a critical role in a superconducting-based nuclear fusion device. As the temperature of superconducting magnets increases with a change in current, it is important to predict their temperature to prevent excessive temperature rise of coils and operate them efficiently. We present Multi-Scale Recurrent Transformer(MSR-Transformer) system, a deep learning model for...
Extensive studies on regulations of the plasma profile by flucating modes may shed light on the plasma control techniques for ameliorating impurity content and plasma performance [1]. In this report, we present the data processing of radially distributed BES measurements related to the two-dimension mapping of the avalanche structure and its related impurity analysis in the HL-2A neutral beam...
Empirical scaling of the thermal energy confinement time $\tau_{E_{th}}$ in tokamak H-mode plasmas, determined from multi-machine databases, remains a convenient tool for studying the dependencies of $\tau_{E_{th}}$ and for predicting confinement based on experimental data. Based on regression analysis, the approach is essentially data-driven, but this does not prevent the incorporation of...
