Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Data-based stochastic model reduction for the Kuramoto--Sivashinsky
  equation   The problem of constructing data-based, predictive, reduced models for the
Kuramoto-Sivashinsky equation is considered, under circumstances where one has
observation data only for a small subset of the dynamical variables. Accurate
prediction is achieved by developing a discrete-time stochastic reduced system,
based on a NARMAX (Nonlinear Autoregressive Moving Average with eXogenous
input) representation. The practical issue, with the NARMAX representation as
with any other, is to identify an efficient structure, i.e., one with a small
number of terms and coefficients. This is accomplished here by estimating
coefficients for an approximate inertial form. The broader significance of the
results is discussed.

10
The categories of this paper are: q-bio
1
The categories of this paper are: cs
6
The categories of this paper are: astro-ph
25
The categories of this paper are: patt-sol
17
The categories of this paper are: q-fin
23
The categories of this paper are: solv-int
18
The categories of this paper are: econ
22
The categories of this paper are: chao-dyn
8
The categories of this paper are: hep-ex
36
The categories of this paper are: cmp-lg
32
The categories of this paper are: atom-ph
27
The categories of this paper are: comp-gas
13
The categories of this paper are: quant-ph
16
The categories of this paper are: stat
35
The categories of this paper are: mtrl-th
14
The categories of this paper are: math-ph