Speaker 1: Abhilash Mathews
Tuesday, November 3, 2020
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. We demonstrates that physics-informed neural networks can accurately learn turbulent field dynamics consistent with the two-fluid theory from just partial observations of a plasma's electron density and temperature. This novel computational tool is being developed for plasma diagnosis and model validation in challenging thermonuclear environments, and initial results from analysis of a synthetic plasma will be presented.