Uncovering turbulent plasma dynamics via deep learning from partial observationshttps://doi.org/10.7910/DVN/JNMLN2Abhilash Mathews, Manaure Francisquez, Jerry Hughes, David Hatch, Ben Zhu, Barrett RogersHarvard Dataverse2021-05-242022-11-11T09:07:17ZOne of the most intensely studied aspects of magnetic confinement fusion is edge plasma turbulence which is critical to reactor performance and operation. Drift-reduced Braginskii two-fluid theory has for decades been widely applied to model boundary plasmas with varying success. Towards better understanding edge turbulence in both theory and experiment, we demonstrate that a novel multi-network physics-informed deep learning framework constrained by partial differential equations can accurately learn turbulent fields consistent with the two-fluid theory from partial observations of electron pressure which is not otherwise possible using conventional equilibrium models. This technique presents a novel paradigm for the advanced design of plasma diagnostics and validation of magnetized plasma turbulence theories in challenging thermonuclear environments.Physicsedge transportmachine learningmagnetized plasmasscrape-off layer plasmastwo fluid theory2021-05-24This dataset is made available without information on how it can be used. You should communicate with the Contact(s) specified before use.