论文标题
通过深入了解部分观察，揭示湍流的等离子体动力学
Uncovering turbulent plasma dynamics via deep learning from partial observations
论文作者
论文摘要
磁性约束融合的最深入研究的方面之一是边缘等离子体湍流，这对于反应堆性能和操作至关重要。数十年来，漂流降低的braginskii两流体理论已被广泛应用于模型边界等离子体，并取得了不同的成功。为了更好地理解理论和实验中的边缘湍流，我们证明了由部分微分方程约束的物理知识的神经网络可以准确地学习与合成等离子体的电子密度的部分观察到与常规平衡模型相比，仅部分观察到合成等离子体的电子密度和温度。这些技术为血浆诊断的高级设计和在挑战性热核环境中磁化等离子体湍流理论的验证提供了一种新颖的范式。
One of the most intensely studied aspects of magnetic confinement fusion is edge plasma turbulence which is critical to reactor performance and operation. Drift-reduced Braginskii two-fluid theory has for decades been widely applied to model boundary plasmas with varying success. Towards better understanding edge turbulence in both theory and experiment, we demonstrate that physics-informed neural networks constrained by partial differential equations can accurately learn turbulent fields consistent with the two-fluid theory from just partial observations of a synthetic plasma's electron density and temperature in contrast with conventional equilibrium models. These techniques present a novel paradigm for the advanced design of plasma diagnostics and validation of magnetized plasma turbulence theories in challenging thermonuclear environments.
