Open AccessA dimensionality reduction algorithm for mapping tokamak operational regimes using a variational autoencoder (VAE) neural network
Author(s) -
Yumou Wei,
J.P. Levesque,
Chris Hansen,
M. E. Mauel,
G.A. Navratil
Publication year - 2021
Publication title -
nuclear fusion
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.774
H-Index - 120
eISSN - 1741-4326
pISSN - 0029-5515
DOI - 10.1088/1741-4326/ac3296
Subject(s) - tokamak , autoencoder , computer science , dimensionality reduction , artificial neural network , reduction (mathematics) , control theory (sociology) , boundary (topology) , stability (learning theory) , artificial intelligence , algorithm , physics , plasma , control (management) , mathematics , machine learning , mathematical analysis , geometry , quantum mechanics
A variational autoencoder (VAE) is a type of unsupervised neural network which is able to learn meaningful data representations in a reduced dimensional space. We present an application of VAE in identifying the operational stability boundary of tokamak plasma discharges. This model was implemented using a dataset of over 3000 discharges from the high beta tokamak-extended pulse (HBT-EP) device. We found the VAE model to be capable of forming a continuous low-dimensional operational space map and identifying the operational boundaries using a specified warning time window. By projecting the operational parameters onto the same reduced space, this provides an intuitive way for the machine operator or an automated control system to perform disruption avoidance using a relevant control actuator as a discharge approaches a boundary. Pre-programmed GPU control experiments were conducted to demonstrate this control technique using HBT-EP’s saddle control coils as a horizontal position actuator, showing the ability to avoid the oncoming disruptive event and extend the duration of the discharge.