Full Shot Predictions for the DIII-D Tokamak via Deep Recurrent Networks

arXiv:2404.12416v1 Announce Type: cross
Abstract: Although tokamaks are one of the most promising devices for realizing nuclear fusion as an energy source, there are still key obstacles when it comes to understanding the dynamics of the plasma and controlling it. As such, it is crucial that high quality models are developed to assist in overcoming these obstacles. In this work, we take an entirely data driven approach to learn such a model. In particular, we use historical data from the …

abstract arxiv cs.lg devices dynamics energy fusion key networks nuclear nuclear fusion obstacles physics.plasm-ph plasma predictions quality tokamak type understanding via