# Experiment data-driven modeling of tokamak discharge in EAST

arXiv: https://arxiv.org/abs/2007.10552 · HTML: https://mlsanity.com/paper/2007.10552/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jul 2020.

## Built on by (most cited first, 2 of 2)

- [A machine-learning-based tool for last closed-flux surface reconstruction on tokamaks](https://mlsanity.com/paper/2207.05695/) (2022)
- [TokaMark: A Comprehensive Benchmark for MAST Tokamak Plasma Models](https://mlsanity.com/paper/2602.10132/) (2026)

## Abstract (arXiv)

A model for tokamak discharge through deep learning has been done on a superconducting long-pulse tokamak (EAST). This model can use the control signals (i.e. Neutral Beam Injection (NBI), Ion Cyclotron Resonance Heating (ICRH), etc) to model normal discharge without the need for doing real experiments. By using the data-driven methodology, we exploit the temporal sequence of control signals for a large set of EAST discharges to develop a deep learning model for modeling discharge diagnostic signals, such as electron density ne, store energy Wmhd and loop voltage Vloop. Comparing the similar methodology, we use Machine Learning techniques to develop the data-driven model for discharge modeling rather than disruption prediction. Up to 95% similarity was achieved for Wmhd. The first try showed promising results for modeling of tokamak discharge by using the data-driven methodology. The data-driven methodology provides an alternative to physical-driven modeling for tokamak discharge modeling.