AI 中文总结
该研究基于双流体漂移减少的布拉金斯基模型，开发机器学习驱动的代理算子，通过训练电阻率条件柯普曼神经算子构建逐场模型，再现了部分短时间统计特征，为边界等离子体湍流诊断提供快速模拟器，但长时间动态闭合问题仍未解决。
AI 中文摘要
基于双流体漂移减少的布拉金斯基模型，开发了用于边界等离子体湍流三维、非线性、通量驱动模拟的机器学习驱动代理算子。在全球布拉金斯基求解器（GBS）模拟上训练电阻率条件柯普曼神经算子（KNOs），涵盖低到高电阻率范围。为等离子体密度、电子温度、电势和涡度构建单独的逐场模型。在保留的电阻率下评估表明，代理再现了关键的短时间统计特征，包括强一步一致性、光谱趋势和降低的压力梯度诊断。场相关限制仍然存在，涡度差异最大，自回归展开逐渐偏离参考模拟。结果表明，电阻率条件逐场神经算子为选定的边界等离子体湍流诊断提供了有用的快速模拟器，而稳定的长时间动态闭合仍未解决。
英文摘要
Machine-learning-driven surrogate operators are developed for three-dimensional, nonlinear, flux-driven simulations of boundary plasma turbulence based on the two-fluid drift-reduced Braginskii model. Resistivity-conditioned Koopman neural operators (KNOs) are trained on Global Braginskii Solver (GBS) simulations, spanning low- to high-resistivity regimes. Separate fieldwise models are constructed for plasma density, electron temperature, electric potential, and vorticity. Evaluation at a held-out resistivity shows that the surrogates reproduce key short-horizon statistical features, including strong one-step agreement, spectral trends, and reduced pressure-gradient diagnostics. Field-dependent limitations remain, with vorticity showing the largest discrepancies and autoregressive rollout progressively departing from the reference simulation. The results demonstrate that resistivity-conditioned fieldwise neural operators provide useful fast emulators for selected boundary-plasma turbulence diagnostics, while stable long-horizon dynamical closure remains unresolved.
