用深度学习从宏观等离子体数据重建电子离子能量分布，实现非侵入式精准诊断。
Data-Driven Reconstruction of Spatially Resolved Electron and Ion Energy Distributions from Macroscopic Plasma Quantities with Deep Neural Networks
- 构建深度神经网络映射宏观测量值到微观能量分布。
- FNO模型表现最佳，还原度与模拟数据高度一致。
- 适用于等离子体诊断与替代仿真建模，适合等离子体研究者。
空间分辨的电子/离子能量分布函数（EEDFs/IEDFs）为低温等离子体（LTPs）提供关键动能信息，对输运、化学反应速率和表面相互作用至关重要。尽管动力学模拟可直接解析这些分布，实验测量仍具挑战性，常需侵入式探测、空间受限或假设分布形态（如麦克斯韦分布）。然而，借助先进诊断技术，可非侵入地获取空间分辨的宏观等离子体可观测量。本文研究核心逆问题：易测的宏观量是否蕴含足够信息以重构底层动能状态。我们采用深度学习框架，基于2D-3V PIC-MCC模拟生成的配对数据集（包含2D宏观量与空间分辨的EDFs），训练三种代表性学习范式——U-Net、FNO与MeshGraphNet，学习从宏观量到空间分辨EDFs的非线性映射。预测结果在整体等离子体与鞘层特征上均与PIC-MCC参考数据高度吻合，其中FNO表现最优。除传统指标外，物理验证表明重构的EDFs准确恢复了密度、温度及反应系数。结果证明，宏观可观测量编码了足够信息以推断LTP中重要动能特性，为代理动力学建模与新一代等离子体诊断奠定基础。
原文摘要 · Abstract (English)
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.
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