Uncovering edge plasma dynamics via deep learning from partial observations

One of the most intensely studied aspects of magnetic confinement fusion is edge plasma behaviour, which is critical to reactor performance and operation. Drift-reduced Braginskii two-fluid theory has for decades been widely used to model edge plasmas with varying success. This Letter demonstrates that physics-informed neural networks can accurately learn turbulent field dynamics consistent with the two-fluid theory from just partial observations of a synthetic plasma's electron density and temperature for plasma diagnosis and model validation in challenging thermonuclear environments.