Uncovering turbulent plasma dynamics via deep learning from partial observations.
Uncovering turbulent plasma dynamics via deep learning from partial observations.
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通过部分观察的深度学习揭示湍流等离子体动力学。
DOI：
10.1103/physreve.104.025205
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Barrett Rogers
中科院分区：
文献类型：
--
作者：
A. Mathews;M. Francisquez;Jerry Hughes;David Hatch;Ben Zhu;Barrett Rogers
磁约束核聚变研究最深入的方面之一是边缘等离子体湍流，它对反应堆的性能和操作至关重要。几十年来，漂移减少的Braginskii双流体理论已被广泛应用于边界等离子体模型，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，我们证明了受偏微分方程约束的物理学深度学习框架可以从电子压力的部分观测中准确地学习与双流体理论一致的湍流场，这是使用传统平衡模型无法实现的。该技术为等离子体诊断的先进设计和具有挑战性的热核环境中磁化等离子体湍流理论的验证提供了范例。
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 a physics-informed deep learning framework constrained by partial differential equations can accurately learn turbulent fields consistent with the two-fluid theory from partial observations of electron pressure which is not otherwise possible using conventional equilibrium models. This technique presents a paradigm for the advanced design of plasma diagnostics and validation of magnetized plasma turbulence theories in challenging thermonuclear environments.
用于偏滤器等离子体特性二维推断的多重诊断贝叶斯分析的开发和模拟
影响因子：
2.2
作者：
Bowman C
通讯作者：
Bowman C