# Full-pulse Tomographic Reconstruction with Deep Neural Networks

arXiv: https://arxiv.org/abs/1802.02242 · HTML: https://mlsanity.com/paper/1802.02242/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Feb 2018.

## Built on by (most cited first, 1 of 1)

- [Machine Learning for Detection of 3D Features using sparse X-ray data](https://mlsanity.com/paper/2206.02564/) (2022)

## Abstract (arXiv)

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.