Abstract A neural network model of the effects of neutral beam injection on DIII-D has been developed. The training and testing data used by the model have been generated by the NUBEAM module of TRANSP for experimental discharges from the 2018 DIII-D campaign. Using a principle component analysis to reduce the dimensionality of profile data, the model has been shown to reproduce the results of the Monte Carlo code NUBEAM with a high level of accuracy and an execution time orders of magnitude faster than the execution time of NUBEAM. This makes the neural network model uniquely suited to applications in model-based scenario planning (off-line) and active control (on-line), where a large number of simulation runs are required by the associated optimization tasks that need to be performed before and during the discharge.

中文翻译：

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使用神经网络的 DIII-D 中 NUBEAM 功能的加速版本

摘要 已经开发了中性束注入对 DIII-D 影响的神经网络模型。该模型使用的训练和测试数据由 TRANSP 的 NUBEAM 模块生成，用于 2018 年 DIII-D 活动的实验放电。使用主成分分析来降低剖面数据的维数，该模型已被证明能够以高精度重现蒙特卡罗代码 NUBEAM 的结果，并且执行时间比 NUBEAM 的执行时间快几个数量级。这使得神经网络模型特别适用于基于模型的场景规划（离线）和主动控制（在线）中的应用，在这些应用中，相关的优化任务需要在之前执行的相关优化任务中进行大量的模拟运行。并且在放电过程中。