Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Training convolutional neural networks to estimate turbulent sub-grid
  scale reaction rates   This work presents a new approach for premixed turbulent combustion modeling
based on convolutional neural networks (CNN). We first propose a framework to
reformulate the problem of subgrid flame surface density estimation as a
machine learning task. Data needed to train the CNN is produced by direct
numerical simulations (DNS) of a premixed turbulent flame stabilized in a
slot-burner configuration. A CNN inspired from a U-Net architecture is designed
and trained on the DNS fields to estimate sub-grid scale wrinkling. It is then
tested on an unsteady turbulent flame where the mean inlet velocity is
increased for a short time and the flame must react to a varying turbulent
incoming flow. The CNN is found to efficiently extract the topological nature
of the flame and predict subgrid scale wrinkling, outperforming classical
algebraic models. This method can be seen as a data-driven extension of dynamic
formulations, where topological information was extracted in a hand-designed
fashion.

6
The categories of this paper are: astro-ph
21
The categories of this paper are: adap-org
14
The categories of this paper are: math-ph
26
The categories of this paper are: bayes-an
37
The categories of this paper are: supr-con
31
The categories of this paper are: ao-sci
34
The categories of this paper are: chem-ph
17
The categories of this paper are: q-fin
25
The categories of this paper are: patt-sol
23
The categories of this paper are: solv-int
35
The categories of this paper are: mtrl-th
10
The categories of this paper are: q-bio
2
The categories of this paper are: math
24
The categories of this paper are: dg-ga
36
The categories of this paper are: cmp-lg
29
The categories of this paper are: funct-an