Full Shot Predictions for the DIII-D Tokamak via Deep Recurrent Networks
arxiv(2024)
摘要
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 DIII-D tokamak to train
a deep recurrent network that is able to predict the full time evolution of
plasma discharges (or "shots"). Following this, we investigate how different
training and inference procedures affect the quality and calibration of the
shot predictions.
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