  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 $n_{e}$, store energy $W_{mhd}$ and loop voltage $V_{loop}$. 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 $W_{mhd}$. 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.
