用于基态ITG分支复本征频率识别与模结构重建的物理信息神经网络

中文摘要 AI 辅助

物理信息神经网络（PINNs）结合稀疏观测数据与物理方程，为复杂等离子体过程建模及未知物理量推断提供了重要方法。高约束模托卡马克的陡梯度台基与等离子体约束及边缘输运密切相关，分析该区域的离子温度梯度（ITG）漂移波需同时识别复本征频率并重建二维复值模场。局域高频振荡、实虚部强耦合及模场与本征频率间的非线性耦合对PINN的表示能力和联合优化提出挑战。为应对这些挑战，本文提出一种结合傅里叶特征编码、复值特征传播及三阶段训练的物理信息神经框架。在稀疏观测和物理约束下，该框架联合求解了代表性基态ITG分支的复本征频率与模场。实验表明，该框架可准确恢复目标复本征频率与二维复值模场，且优于代表性PINN基线，为分析高阶及多分支漂移波模提供了基础。

英文摘要

Physics-informed neural networks (PINNs) combine sparse observations with physical equations, providing an important approach for modeling complex plasma processes and inferring unknown physical quantities. The steep-gradient pedestal of high-confinement-mode tokamaks is closely linked to plasma confinement and edge transport. Analyzing ion-temperature-gradient (ITG) drift waves in this region requires jointly identifying complex eigenfrequencies and reconstructing two-dimensional complex-valued mode fields. Localized high-frequency oscillations, strong real-imaginary coupling, and nonlinear coupling between the mode field and eigenfrequency challenge PINN representation and joint optimization. To address these challenges, we propose a physics-informed neural framework combining Fourier feature encoding, complex-valued feature propagation, and three-stage training. Under sparse observations and physical constraints, it jointly solves for the complex eigenfrequency and mode field of a representative ground-state ITG branch. Experiments show that the framework accurately recovers the target complex eigenfrequency and two-dimensional complex-valued mode field and outperforms representative PINN baselines. It also provides a basis for analyzing higher-order and multiple-branch drift-wave modes.

发表机构

• School of Artificial Intelligence, Anhui University（安徽大学人工智能学院）
• School of Computer Science and Technology, Anhui University（安徽大学计算机科学与技术学院）
• School of Physics and Electronic Information, Anhui Normal University（安徽师范大学物理与电子信息学院）
• Institute of Plasma Physics, Chinese Academy of Sciences（中国科学院等离子体物理研究所）

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