用深度学习逆向设计高纵横比托卡马克，提升等离子体约束性能。
Using Deep Learning to Design High Aspect Ratio Fusion Devices
- 通过概率混合密度网络解决逆向设计的多解问题。
- 生成的配置在低拉伸、高旋转变换下表现稳定，支持有限β值。
- 适合等离子体物理与核聚变装置优化研究者参考。
融合装置设计通常依赖计算成本高昂的模拟。采用高纵横比模型可减少自由参数数量，尤其适用于非轴对称磁场的托卡马克优化，其参数空间大且需满足特定性能指标。然而仍需优化以实现低拉伸、高旋转变换、有限等离子体β值及良好快粒子约束。本文训练机器学习模型，通过求解逆向设计问题——即根据期望性能反推模型输入参数——来构建具有良好约束特性的配置。由于逆向问题解不唯一，采用基于混合密度网络的概率方法。结果表明，该方法可可靠生成优化配置。
原文摘要 · Abstract (English)
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.
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