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, that is, 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.

翻译：聚变装置的设计通常基于计算成本高昂的仿真模拟。采用高纵横比模型可缓解这一问题，此类模型使用较少的自由参数，尤其在仿星器优化中具有优势——此时需优化具有庞大参数空间的非轴对称磁场以满足特定性能指标。然而，仍需通过优化来寻找具有低拉长比、高旋转变换、有限等离子体比压及良好快粒子约束等特性的位形。本研究通过求解逆设计问题（即根据给定目标特性获取模型输入参数集合），训练机器学习模型以构建具有优良约束特性的位形。由于逆问题的解不具唯一性，本研究采用基于混合密度网络的概率方法。结果表明，该方法能可靠地生成优化位形。