Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Uncovering turbulent plasma dynamics via deep learning from partial
  observations   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
physics-informed neural networks constrained by partial differential equations
can accurately learn turbulent fields consistent with the two-fluid theory from
just partial observations of a synthetic plasma's electron density and
temperature in contrast with conventional equilibrium models. These techniques
present a novel paradigm for the advanced design of plasma diagnostics and
validation of magnetized plasma turbulence theories in challenging
thermonuclear environments.

25
The categories of this paper are: patt-sol
1
The categories of this paper are: cs
6
The categories of this paper are: astro-ph
33
The categories of this paper are: plasm-ph
28
The categories of this paper are: alg-geom
17
The categories of this paper are: q-fin
8
The categories of this paper are: hep-ex
18
The categories of this paper are: econ
37
The categories of this paper are: supr-con
10
The categories of this paper are: q-bio
15
The categories of this paper are: nucl-ex
22
The categories of this paper are: chao-dyn
14
The categories of this paper are: math-ph
16
The categories of this paper are: stat
26
The categories of this paper are: bayes-an
31
The categories of this paper are: ao-sci