Experiment data-driven modeling of tokamak discharge in EAST
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A model for tokamak discharge through deep learning has been done on EAST tokamak. This model can use the controlled input signals (i.e. NBI, 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 controlled input signals for a large set of EAST discharges to develop a deep learning model for modeling discharge diagnose signals, such as electron density n_e, store energy W_mhd and loop voltage V_loop. Comparing the similar methodology, we pioneered a state-of-the-art Machine Learning techniques to develop the data-driven model for discharge modeling. Up to 95 achieved for W_mhd. The first try showed very promising results for modeling of tokamak discharge by using data-driven methodology. This is a very good tool for the ultimate goal of machine learning applied in fusion experiments for plasma discharge modeling and discharge planning in the future.
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