# Deep Learning for Plasma Tomography and Disruption Prediction from Bolometer Data

arXiv: https://arxiv.org/abs/1910.13257 · HTML: https://mlsanity.com/paper/1910.13257/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Oct 2019.

## Built on by (most cited first, 5 of 5)

- [Deep Learning Architecture Based Approach For 2D-Simulation of Microwave Plasma Interaction](https://mlsanity.com/paper/2206.01263/) (2022)
- [TokaMark: A Comprehensive Benchmark for MAST Tokamak Plasma Models](https://mlsanity.com/paper/2602.10132/) (2026)
- [Transferable Cross-Tokamak Disruption Prediction with Deep Hybrid Neural Network Feature Extractor](https://mlsanity.com/paper/2208.09594/) (2022)
- [Visible-Light Imaging Diagnosis of Neutral Particle Emission Tomography in the Tokamak Divertor: An Efficient Transformer-based Surrogate Model](https://mlsanity.com/paper/2607.22704/) (2026)
- [A Pre-trained Variational Autoencoder for Gyrokinetic Plasma Turbulence Surrogate Modeling](https://mlsanity.com/paper/2609.38438/) (2026)

## Abstract (arXiv)

The use of deep learning is facilitating a wide range of data processing tasks in many areas. The analysis of fusion data is no exception, since there is a need to process large amounts of data collected from the diagnostic systems attached to a fusion device. Fusion data involves images and time series, and are a natural candidate for the use of convolutional and recurrent neural networks. In this work, we describe how CNNs can be used to reconstruct the plasma radiation profile, and we discuss the potential of using RNNs for disruption prediction based on the same input data. Both approaches have been applied at JET using data from a multi-channel diagnostic system. Similar approaches can be applied to other fusion devices and diagnostics.