Using Deep Learning to Design High Aspect Ratio Fusion Devices (Dataset)

This dataset was developed for the work entitled Using Deep Learning to Design High Aspect Ratio Fusion Devices. A small explanation is hereby given: The design of fusion devices is typically based on computationally expensive simulations. This can be alleviated using high aspect ratio models that employ a reduced number of free parameters, especially in the case of stellarator optimization where non-axisymmetric magnetic fields with a large parameter space are optimized to satisfy certain performance criteria. However, optimization is still required to find configurations with properties such as low elongation, high rotational transform, finite plasma beta, and good fast particle confinement. In this work, we train a machine learning model to construct configurations with favorable confinement properties by finding a solution to the inverse design problem: obtaining a set of model input parameters for given desired properties. Since the solution of the inverse problem is non-unique, a probabilistic approach, based on mixture density networks, is used. It is shown that optimized configurations can be generated reliably using this method.本数据集专为题为《使用深度学习设计高纵横比聚变装置》的研究工作开发，兹作简要说明： 聚变装置的设计通常依赖计算成本高昂的数值模拟。采用自由参数更少的高纵横比模型，可缓解这一问题，尤其在仿星器（stellarator）优化场景中：此类场景需优化参数空间广阔的非轴对称磁场，以满足特定性能准则。然而，仍需通过优化寻得具备低拉长比、高旋转变换、有限等离子体β（plasma beta）以及优异快粒子约束（fast particle confinement）特性的构型。 本研究通过求解逆设计问题（inverse design problem），训练机器学习模型以构建具备优良约束特性的构型：即针对给定的期望性能参数，求解得到一组模型输入参数。由于逆问题的解并非唯一，本研究采用基于混合密度网络（mixture density networks）的概率化方法。实验结果表明，使用该方法可稳定生成经过优化的聚变装置构型。