  Plasma tomography consists in reconstructing the 2D radiation profile in a
poloidal cross-section of a fusion device, based on line-integrated
measurements along several lines of sight. The reconstruction process is
computationally intensive and, in practice, only a few reconstructions are
usually computed per pulse. In this work, we trained a deep neural network
based on a large collection of sample tomograms that have been produced at JET
over several years. Once trained, the network is able to reproduce those
results with high accuracy. More importantly, it can compute all the
tomographic reconstructions for a given pulse in just a few seconds. This makes
it possible to visualize several phenomena -- such as plasma heating,
disruptions and impurity transport -- over the course of a discharge.
