物理信息神经网络用于推断 stellarator 设备 scrape-off 层的垂直能量传导率
Physics-Informed Neural Networks to Infer the Perpendicular Energy Conductivity in the Scrape-Off Layer of Stellarator Devices
物理学家和工程师们可以看看这个，用神经网络来推断等离子体物理中的关键参数，比传统方法更高效。
本文提出了一种反向物理信息神经网络（PINN）框架，用于推断 scrape-off 层（SOL）垂直热导率 κ⊥(n,T) 与等离子体密度和温度的关系。该框架结合了电子密度和温度的径向轮廓测量值与一维 SOL 运输方程的残差，使推断的导热率同时受到测量值和底层传输模型的约束。三个神经网络被同时训练：两个分别重建温度和密度轮廓作为径向坐标和输运功率的函数，第三个则表示有效导热率作为局部密度和温度的函数。该框架首先使用从预设导热率函数生成的合成数据进行了验证，允许将推断的 κ⊥(n,T) 与真实值直接比较，在数据约束区域内误差低于 10%。Bootstrap 重采样被证明是预测可靠性和一致性的实用指标。对训练所用等离子体轮廓数量和每个轮廓的径向测量位置数量的扫描，识别了重建精度和数据可用性之间的实用权衡。最后，该方法应用于 TJ-II stellarator 的实验数据集，该数据集使用氦束诊断器获得。这项探索性应用提供了有效 SOL 导热率的一个初步估计，并说明了反向 PINN 从等离子体边缘测量中提取传输信息的能力。
Physics-Informed Neural Networks to Infer the Perpendicular Energy Conductivity in the Scrape-Off Layer of Stellarator Devices
In this work, we develop an inverse Physics-Informed Neural Network (PINN) framework to infer the dependence of the scrape-off layer (SOL) perpendicular heat conductivity on plasma density and temperature, $κ_\perp(n,T)$. The method combines radial profile measurements of electron density and temperature with the residual of a reduced one-dimensional SOL transport equation, so that the inferred conductivity is constrained by both the measurements and the underlying transport model. Three neural networks are trained simultaneously: two reconstruct the temperature and density profiles as functions of the radial coordinate and transported power, while a third represents the effective conductivity as a function of the local density and temperature. The framework is first validated using synthetic data generated from a prescribed conductivity function, allowing the inferred $κ_\perp(n,T)$ to be compared directly with the ground truth. The model recovers the imposed functional dependence with errors below $10~\%$ in the data-constrained region. Bootstrap resampling is shown to provide a practical indicator of prediction reliability and consistency. A scan in the number of plasma profiles used for training and the number of radial measurement positions per profile identifies a practical trade-off between reconstruction accuracy and data availability. Finally, the method is applied to an experimental dataset from the TJ-II stellarator obtained with the helium-beam diagnostic. This exploratory application provides an initial estimate of the effective SOL conductivity and illustrates the potential of inverse PINNs for extracting transport information from plasma edge measurements.