Uncovering turbulent plasma dynamics via deep learning from partial observations (doi:10.7910/DVN/JNMLN2)

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Uncovering turbulent plasma dynamics via deep learning from partial observations

doi:10.7910/DVN/JNMLN2

Harvard Dataverse

2021-05-24

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Abhilash Mathews, Manaure Francisquez, Jerry Hughes, David Hatch, Ben Zhu, Barrett Rogers, 2021, "Uncovering turbulent plasma dynamics via deep learning from partial observations", https://doi.org/10.7910/DVN/JNMLN2, Harvard Dataverse, V2

Uncovering turbulent plasma dynamics via deep learning from partial observations

doi:10.7910/DVN/JNMLN2

Abhilash Mathews, Manaure Francisquez, Jerry Hughes, David Hatch, Ben Zhu, Barrett Rogers

Harvard Dataverse

https://doi.org/10.7910/DVN/JNMLN2

Physics, edge transport, machine learning, magnetized plasmas, scrape-off layer plasmas, two fluid theory

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.

This dataset is made available without information on how it can be used. You should communicate with the Contact(s) specified before use.

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