物理信息神经网络推断仿星器装置刮削层垂直能量导率
Physics-Informed Neural Networks to Infer the Perpendicular Energy Conductivity in the Scrape-Off Layer of Stellarator Devices
- CIEMAT（西班牙能源、环境与技术研究中心）
- Harvard John A. Paulson School of Engineering and Applied Sciences（哈佛大学约翰·A·保尔森工程与应用科学学院）
- Laboratorio Nacional de Fusión, CIEMAT（CIEMAT国家聚变实验室）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究开发逆物理信息神经网络框架，结合测量与输运方程推断仿星器刮削层垂直热导率，验证误差低于10%，并应用于TJ-II实验数据。
AI中文摘要：
在本工作中，我们开发了一个逆物理信息神经网络（PINN）框架，以推断刮削层（SOL）垂直热导率对等离子体密度和温度的依赖关系，即 $\kappa_\perp(n,T)$。该方法将电子密度和温度的径向剖面测量与简化的一维SOL输运方程的残差相结合，使得推断出的导率同时受到测量数据和底层输运模型的约束。三个神经网络被同时训练：其中两个网络将温度和密度剖面重构为径向坐标和输运功率的函数，而第三个网络则表示为局部密度和温度函数的有效导率。该框架首先使用由预设导率函数生成的合成数据进行验证，从而可以将推断出的 $\kappa_\perp(n,T)$ 与真实值直接进行比较。模型在数据约束区域内恢复了所施加的函数依赖关系，误差低于 $10\\%$。结果表明，自助重采样可以提供预测可靠性和一致性的实用指标。对用于训练等离子体剖面数量和每个剖面的径向测量位置数量的扫描，揭示了重建精度与数据可用性之间的实际权衡。最后，该方法被应用于来自TJ-II仿星器氦束诊断的实验数据集。这一探索性应用提供了有效SOL导率的初步估计，并展示了逆PINN在从等离子体边缘测量中提取输运信息方面的潜力。
英文摘要：
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.
