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Data-Driven Reconstruction of Spatially Resolved Electron and Ion Energy Distributions from Macroscopic Plasma Quantities with Deep Neural Networks

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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.

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arXiv Machine Learning
Aug 18

Data-Driven Reconstruction of Spatially Resolved Electron and Ion Energy Distributions from Macroscopic Plasma Quantities with Deep Neural Networks

arXiv:2608. 16519v1 Announce Type: cross 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.

By Libin Varghese, Kaushik Prajapati, Bhaskar Chaudhury
arXiv Machine Learning
Aug 21

Heteroscedastic Neural Surrogate Modeling for Robust and Rapid Bayesian Inference in Fusion Plasma Diagnostics

arXiv:2608. 19377v1 Announce Type: cross Abstract: Bayesian inference via Markov Chain Monte Carlo (MCMC) provides effective parameter estimation, but its real-time application in complex physical systems is hindered by heavy computational bottlenecks and extreme sensitivity to statistical noise.

By Liyun Zhang, Naoya Mamada, Kentaro Sakai, Takeo Hoshi, Toru Aonishi
arXiv AI
Sep 7

GyroSwin: 5D Surrogates for Gyrokinetic Plasma Turbulence Simulations

GyroSwin is a scalable 5‑dimensional neural surrogate that models nonlinear gyrokinetic plasma turbulence, a key challenge for nuclear fusion research. It extends Vision Transformers to 5D, incorporates cross‑attention and latent 3D↔5D interactions, and uses channelwise mode separation inspired by nonlinear physics. The model outperforms traditional reduced numerics in heat‑flux prediction, captures turbulent energy cascades, and cuts the computational cost of full gyrokinetic simulations by three orders of magnitude while remaining physically verifiable.

By Fabian Paischer, Gianluca Galletti, William Hornsby, Paul Setinek, Lorenzo Zanisi, Naomi Carey, Stanislas Pamela, Johannes Brandstetter