Problem
Research questions and friction points this paper is trying to address.
Electron Energy Distribution Function
Ion Energy Distribution Function
Low-temperature Plasmas
Inverse Problem
Macroscopic Plasma Observables
Data-Driven Reconstruction of Spatially Resolved Electron and Ion Energy Distributions from Macroscopic Plasma Quantities with Deep Neural Networks
🤖 AI Summary
This study addresses the challenge of measuring kinetic energy distributions in low-temperature plasmas by proposing a deep learning-based nonlinear inverse mapping method. Utilizing 2D-3V PIC-MCC data, U-Net, FNO, and MeshGraphNet models were trained to reconstruct microscopic energy distributions from macroscopic observables. This work validates for the first time that macroscopic quantities contain sufficient information to infer kinetic properties, establishing a new paradigm for surrogate kinetic modeling. Results demonstrate that the reconstructed distributions accurately reproduce bulk and sheath characteristics, with FNO exhibiting superior performance and key physical parameters aligning closely with reference data. These findings offer a novel pathway for next-generation plasma diagnostics.
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Deep Learning Inverse Problem
Spatially Resolved EDF Reconstruction
Fourier Neural Operator
Non-invasive Plasma Diagnostics
PIC-MCC Data Generation
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