基于宏观等离子体量的空间分辨电子与离子能量分布的深度学习数据驱动重建
Data-Driven Reconstruction of Spatially Resolved Electron and Ion Energy Distributions from Macroscopic Plasma Quantities with Deep Neural Networks
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中文总结 AI 辅助
本研究采用U-Net、FNO、MeshGraphNet三种深度学习模型，通过2D-3V PIC-MCC模拟数据学习宏观等离子体可观测量到空间分辨EEDFs/IEDFs的非线性映射，证实宏观等离子体量可推断低温等离子体关键动力学特性，FNO表现最优。
中文摘要 AI 辅助
空间分辨电子能量分布函数（EEDFs）和离子能量分布函数（IEDFs）提供了低温等离子体（LTPs）的关键动力学信息，在确定输运、化学反应速率及等离子体-表面相互作用中发挥核心作用。尽管动力学模拟可直接解析这些分布，但实验测量仍具挑战性，常为侵入式、空间受限，或需对分布形状（如麦克斯韦分布）作出假设。然而，多种宏观等离子体可观测量可通过先进诊断技术非侵入式测量，提供等离子体状态的空间分辨信息。因此，一个重要的逆问题是：易测量的宏观等离子体量是否包含足够信息以重建潜在动力学状态。本研究通过深度学习框架学习从空间分辨宏观等离子体可观测量到对应空间分辨EEDFs/IEDFs的非线性映射，探究该问题。利用2D-3V PIC-MCC模拟生成包含二维宏观可观测量和空间分辨EDFs的配对数据集，本研究采用U-Net、FNO和MeshGraphNet三种代表性学习范式学习该逆映射。预测的EDFs能很好地复现体等离子体和鞘层特性，与PIC-MCC参考数据吻合良好，其中FNO表现最优。除传统指标外，基于物理的验证表明，重建的EDFs可准确恢复对应密度、温度及速率系数。这些结果证明，宏观等离子体可观测量编码了推断LTPs重要动力学特性的足够信息，为代理动力学建模和下一代等离子体诊断提供了潜在基础。
英文摘要
Spatially resolved EEDFs/IEDFs provide essential kinetic information about low-temperature plasmas (LTPs) and play a central role in determining transport, chemical reaction rates, and plasma surface interactions. While kinetic simulations directly resolve these distributions, experimental measurements remain challenging and are often invasive, spatially limited, or require assumptions regarding the distribution shape such as a Maxwellian. However, several macroscopic plasma observables can be measured non-invasively using advanced diagnostic techniques, providing spatially resolved information about the plasma state. An important inverse problem is therefore whether readily measurable macroscopic plasma quantities contain sufficient information to reconstruct the underlying kinetic state. In this work, we investigate this problem by learning a nonlinear mapping from spatially resolved macroscopic plasma observables to the corresponding spatially resolved EEDFs/IEDFs using a deep learning framework. Paired datasets comprising 2D macroscopic observables and spatially resolved EDFs are generated using 2D-3V PIC-MCC simulations. Three representative learning paradigms, a U-Net, a FNO, and a MeshGraphNet, are employed in this study to learn this inverse mapping. The predicted EDFs reproduce both bulk plasma and sheath characteristics with good agreement to the PIC-MCC reference data, with the FNO providing the best overall performance. Beyond conventional metrics, physics-based validation demonstrates that the reconstructed EDFs accurately recover the corresponding density and temperature, and rate coefficients. These results demonstrate that macroscopic plasma observables encode sufficient information to infer important kinetic properties in LTPs, providing a potential foundation for surrogate kinetic modeling and next-generation plasma diagnostics.
发表机构
- Group in Computational Science and HPC, Dhirubhai Ambani University（计算科学与高性能计算组，迪鲁巴伊·安巴尼大学）
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