Cycle-Consistent and Uncertainty-Aware Neural Surrogates for Tokamak Edge Plasmas
The boundary and divertor plasma govern how a tokamak exhausts power and particles, setting heat fluxes, target conditions, and the onset of detachment. Predicting these quantities is essential for operating current and future devices, but edge simulations that resolve them are too slow for parameter scans, optimization, or real-time control. Machine-learning surrogates offer a fast alternative, yet most are forward-only: they cannot recover input parameters from observations or assess the reliability of their predictions. We introduce a cycle-consistent neural surrogate for edge plasmas, combining a conditional U-Net forward model with an optimization-based inverse method built on the fr...
| Authors | Abdourahmane Diaw, Sebastian De Pascuale, Jae-Sun Park et al. |
|---|---|
| Primary category | physics.comp-ph |
| Published | 23 July 2026 |
| Last updated at source | 23 July 2026 |
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