Uncovering turbulent plasma dynamics via deep learning from partial observations

Mathews, Abhilash; Francisquez, Manaure; Hughes, Jerry W.; Hatch, David R.; Zhu, Ben; Rogers, Barrett N.

doi:10.1103/physreve.104.025205

Uncovering turbulent plasma dynamics via deep learning from partial observations

Journal Article · Fri Aug 13 00:00:00 EDT 2021 · Physical Review. E

DOI:https://doi.org/10.1103/physreve.104.025205· OSTI ID:1813020

^[1]; ^[2]; ^[3]; Hatch, David R. ^[4]; Zhu, Ben ^[5]; Rogers, Barrett N. ^[6]

MIT Plasma Science and Fusion Center, Cambridge, MA (United States); MIT Plasma Science and Fusion Center
MIT Plasma Science and Fusion Center, Cambridge, MA (United States); Princeton Plasma Physics Lab. (PPPL), Princeton, NJ (United States)
MIT Plasma Science and Fusion Center, Cambridge, MA (United States)
Univ. of Texas, Austin, TX (United States)
Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Dartmouth College, Hanover, NH (United States)

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. Furthermore, this technique presents a novel paradigm for the advanced design of plasma diagnostics and validation of magnetized plasma turbulence theories in challenging thermonuclear environments.

View Accepted Manuscript (DOE)

Research Organization:: Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States); MIT Plasma Science and Fusion Center, Cambridge, MA (United States); Princeton Plasma Physics Laboratory (PPPL), Princeton, NJ (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Fusion Energy Sciences (FES)

Grant/Contract Number:: AC52-07NA27344; FC02-08ER54966; FG02-04ER54742; SC0014264

OSTI ID:: 1813020

Alternate ID(s):: OSTI ID: 1815370
OSTI ID: 1822274

Report Number(s):: LLNL-JRNL-820389

Journal Information:: Physical Review. E, Journal Name: Physical Review. E Journal Issue: 2 Vol. 104; ISSN 2470-0045

Publisher:: American Physical Society (APS)Copyright Statement

Country of Publication:: United States

Language:: English

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