A neural network model of tokamak discharge is developed based on the experimental dataset of a superconducting long-pulse tokamak (EAST) campaign 2016–2018. The purpose is to reproduce the response of diagnostic signals to actuator signals without introducing additional physical models. In the present work, the discharge curves of electron density n _e, stored energy W _mhd, and loop voltage V _loop were reproduced from a series of actuator signals. For n _e and W _mhd, the average similarity between the modeling results and the experimental data achieve 89% and 97%, respectively. The promising results demonstrate that the data-driven methodology provides an alternative to the physical-driven methodology for tokamak discharge modeling. The method presented in the manuscript has the potential of being used for validating the tokamak’s experimental proposals, which could advance and optimize experimental planning and validation.

托卡马克放电的神经网络模型是基于 2016-2018 年超导长脉冲托卡马克 (EAST) 活动的实验数据集开发的。目的是在不引入额外物理模型的情况下重现诊断信号对执行器信号的响应。在目前的工作中，电子密度n _e、储存能量W _mhd和回路电压V _loop的放电曲线是从一系列致动器信号中再现出来的。对于n _e和W _mhd，建模结果与实验数据的平均相似度分别达到了 89% 和 97%。有希望的结果表明，数据驱动的方法为托卡马克放电建模提供了物理驱动方法的替代方案。手稿中提出的方法有可能用于验证托卡马克的实验建议，这可以推进和优化实验计划和验证。