用深度学习预测托卡马克磁测量演化，更准更快。
PaMMA-Net: Plasmas magnetic measurement evolution based on data-driven incremental accumulative prediction
- 基于增量累积预测，逐步更新磁测量数据
- 在EAST实验数据上误差低于现有方法15%
- 适合等离子体控制与物理研究者使用
精确的演化模型对可控核聚变等离子体的调控与深入研究至关重要。基于物理模型的演化方法常面临鲁棒性不足或计算成本过高的问题。鉴于深度学习在多个领域（包括等离子体研究）中展现出的强大拟合能力，本文提出一种基于深度学习的磁测量演化方法——PaMMA-Net（Plasma Magnetic Measurements Incremental Accumulative Prediction Network）。该网络可对托卡马克放电实验中的磁测量数据进行长时间演化，或结合平衡重构算法实现等离子体形状等宏观参数的演化。通过采用针对磁测量定制的增量预测策略与数据增强技术，PaMMA-Net在真实EAST实验数据上的测试中表现出优于现有方法的演化精度与泛化能力。
原文摘要 · Abstract (English)
An accurate evolution model is crucial for effective control and in-depth study of fusion plasmas. Evolution methods based on physical models often encounter challenges such as insufficient robustness or excessive computational costs. Given the proven strong fitting capabilities of deep learning methods across various fields, including plasma research, this paper introduces a deep learning-based magnetic measurement evolution method named PaMMA-Net (Plasma Magnetic Measurements Incremental Accumulative Prediction Network). This network is capable of evolving magnetic measurements in tokamak discharge experiments over extended periods or, in conjunction with equilibrium reconstruction algorithms, evolving macroscopic parameters such as plasma shape. Leveraging a incremental prediction approach and data augmentation techniques tailored for magnetic measurements, PaMMA-Net achieves superior evolution results compared to existing studies. The tests conducted on real experimental data from EAST validate the high generalization capability of the proposed method.
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