Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Turbulence closure modeling with data-driven techniques: Investigation
  of generalizable deep neural networks   Generalizability of machine-learning (ML) based turbulence closures to
accurately predict unseen practical flows remains an important challenge. At
the Reynolds-averaged Navier-Stokes (RANS) level, NN-based turbulence closure
modeling is rendered difficult due to two important reasons: inherent
complexity of the constitutive relation arising from flow-dependent
non-linearity and bifurcations; and, inordinate difficulty in obtaining
high-fidelity data covering the entire parameter space of interest. In this
context, the objective of the work is to investigate the approximation
capabilities of standard moderate-sized fully-connected NNs. We seek to
systematically investigate the effects of: (i) intrinsic complexity of the
solution manifold; (ii) sampling procedure (interpolation vs. extrapolation)
and (iii) optimization procedure. To overcome the data acquisition challenges,
three proxy-physics turbulence surrogates of different degrees of complexity
(yet significantly simpler than turbulence physics) are employed to generate
the parameter-to-solution maps. Even for this simple proxy-physics system, it
is demonstrated that feed-forward NNs require more degrees of freedom than the
original proxy-physics model to accurately approximate the true model even when
trained with data over the entire parameter space (interpolation).
Additionally, if deep fully-connected NNs are trained with data only from part
of the parameter space (extrapolation), their approximation capability reduces
considerably and it is not straightforward to find an optimal architecture.
Overall, the findings provide a realistic perspective on the utility of ML
turbulence closures for practical applications and identify areas for
improvement.

22
The categories of this paper are: chao-dyn
29
The categories of this paper are: funct-an
36
The categories of this paper are: cmp-lg
19
The categories of this paper are: eess
0
The categories of this paper are: hep-ph
28
The categories of this paper are: alg-geom
18
The categories of this paper are: econ
37
The categories of this paper are: supr-con
20
The categories of this paper are: acc-phys
24
The categories of this paper are: dg-ga
26
The categories of this paper are: bayes-an
5
The categories of this paper are: gr-qc
6
The categories of this paper are: astro-ph
23
The categories of this paper are: solv-int
14
The categories of this paper are: math-ph
33
The categories of this paper are: plasm-ph