  Nuclear fusion is the process that powers the sun, and it is one of the best
hopes to achieve a virtually unlimited energy source for the future of
humanity. However, reproducing sustainable nuclear fusion reactions here on
Earth is a tremendous scientific and technical challenge. Special devices --
called tokamaks -- have been built around the world, with JET (Joint European
Torus, in the UK) being the largest tokamak currently in operation. Such
devices confine matter and heat it up to extremely high temperatures, creating
a plasma where fusion reactions begin to occur. JET has over one hundred
diagnostic systems to monitor what happens inside the plasma, and each
30-second experiment (or pulse) generates about 50 GB of data. In this work, we
show how convolutional neural networks (CNNs) can be used to reconstruct the 2D
plasma profile inside the device based on data coming from those diagnostics.
We also discuss how recurrent neural networks (RNNs) can be used to predict
plasma disruptions, which are one of the major problems affecting tokamaks
today. Training of such networks is done on NVIDIA GPUs.
