Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Connecting implicit and explicit large eddy simulations of
  two-dimensional turbulence through machine learning   In this article, we utilize machine learning to dynamically determine if a
point on the computational grid requires implicit numerical dissipation for
large eddy simulation (LES). The decision making process is learnt through
\emph{a priori} training on quantities derived from direct numerical simulation
(DNS) data. In particular, we compute eddy-viscosities obtained through the
coarse graining of DNS quantities and utilize their distribution to categorize
areas that require dissipation. If our learning determines that closure is
necessary, an upwinded scheme is utilized for computing the non-linear
Jacobian. In contrast, if it is determined that closure is unnecessary, a
symmetric and second-order accurate energy and enstrophy preserving Arakawa
scheme is utilized instead. This results in a closure framework that precludes
the specification of any model-form for the small scale contributions of
turbulence but deploys an appropriate numerical dissipation from explicit
closure driven hypotheses. This methodology is deployed for the Kraichnan
turbulence test-case and assessed through various statistical quantities such
as angle-averaged kinetic energy spectra and vorticity structure functions. Our
framework thus establishes a direct link between the use of explicit LES
ideologies for closure and numerical scheme-based modeling of turbulence
leading to improved statistical fidelity of \emph{a posteriori} simulations.

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The categories of this paper are: chem-ph
7
The categories of this paper are: hep-th
16
The categories of this paper are: stat
2
The categories of this paper are: math
6
The categories of this paper are: astro-ph
27
The categories of this paper are: comp-gas
5
The categories of this paper are: gr-qc
35
The categories of this paper are: mtrl-th
30
The categories of this paper are: q-alg
23
The categories of this paper are: solv-int
26
The categories of this paper are: bayes-an
12
The categories of this paper are: nucl-th
28
The categories of this paper are: alg-geom
31
The categories of this paper are: ao-sci
17
The categories of this paper are: q-fin
37
The categories of this paper are: supr-con