10.7910/DVN/JNMLN2 Abhilash Mathews, Manaure Francisquez, Jerry Hughes, David Hatch, Ben Zhu, Barrett Rogers Jerry David Ben Barrett Rogers Harvard Dataverse 2021 Physics edge transport machine learning magnetized plasmas scrape-off layer plasmas two fluid theory 2021-05-24 2022-11-11 172029 124216 318088 4568 44890 170695 172029 127779 142365 362944 251672 8432 150176 318088 368640 60041 6600983 image/png application/x-h5 application/x-h5 application/x-h5 image/png image/png image/png image/png image/png application/x-h5 image/png application/x-h5 image/png application/x-h5 application/x-h5 image/png application/pdf 2.0 Custom terms specific to this dataset One 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. <a href="http://library.psfc.mit.edu/catalog/reports/2020/21ja/21ja011/abstract.php">PSFC REPORT PSFC/JA-21-11</a><br /><br />The work is supported by the Natural Sciences and Engineering Research Council of Canada (NSERC) by the doctoral postgraduate scholarship (PGS D), Manson Benedict Fellowship, and the U.S. Department of Energy (DOE) Office of Science under the Fusion Energy Sciences program by contracts DE-SC0014264, DE-FC02-08ER54966, DE-FG02-04ER54742, and DE-AC52-07NA27344.