70 citations · 183 across the 6 of their papers we have counts for
6 papers
A machine-learning-based tool for last closed-flux surface reconstruction on tokamaks
Chenguang Wan, Zhi Yu, Alessandro Pau +2
Nuclear fusion represents one of the best alternatives for a sustainable source of clean energy. Tokamaks allow to confine fusion plasma with magnetic fields and one of the main ch…
First-principles density limit scaling in tokamaks based on edge turbulent transport and implications for ITER
M. Giacomin, A. Pau, P. Ricci +2
A first-principles scaling law, based on turbulent transport considerations, and a multi-machine database of density limit discharges from the ASDEX Upgrade, JET and TCV tokamaks,…
EAST discharge prediction without integrating simulation results
Chenguang Wan, Zhi Yu, Alessandro Pau +2
In this work, a purely data-driven discharge prediction model was developed and tested without integrating any data or results from simulations. The model was developed based on th…
Plasma Confinement Mode Classification Using a Sequence-to-Sequence Neural Network With Attention
Francisco Matos, Vlado Menkovski, Alessandro Pau +2
In a typical fusion experiment, the plasma can have several possible confinement modes. At the TCV tokamak, aside from the Low (L) and High (H) confinement modes, an additional mod…
Integrated real-time supervisory management for off-normal-event handling and feedback control of tokamak plasmas
T. Vu, F. Felici, C. Galperti +7
For long-pulse tokamaks, one of the main challenges in control strategy is to simultaneously reach multiple control objectives and to robustly handle in real-time (RT) unexpected e…
Classification of tokamak plasma confinement states with convolutional recurrent neural networks
F. Matos, V. Menkovski, F. Felici +2
During a tokamak discharge, the plasma can vary between different confinement regimes: Low (L), High (H) and, in some cases, a temporary (intermediate state), called Dithering (D).…