论文标题
东方Tokamak放电的实验数据驱动的建模
Experiment data-driven modeling of tokamak discharge in EAST
论文作者
论文摘要
通过深度学习进行了托卡马克（Tokamak）排放的模型，已经在超导的长脉冲托卡马克（East）上进行了。该模型可以使用控制信号（即中性束注射（NBI），离子回旋共振加热（ICRH）等）对正常放电进行建模，而无需进行实际实验。通过使用数据驱动的方法，我们利用了大量东放电的控制信号的时间顺序来开发用于建模排放诊断信号的深度学习模型，例如电子密度$ n_ {e} $，存储能量$ w_ {mHd} $ and loop电压$ v_ $ v_ v_ {loop} $。比较类似的方法，我们使用机器学习技术来开发用于放电建模的数据驱动模型，而不是中断预测。对于$ W_ {MHD} $，可实现多达95％的相似性。第一次尝试通过使用数据驱动的方法来表现出有希望的Tokamak放电的结果。数据驱动的方法为Tokamak放电建模提供了替代物理驱动的建模。
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.
