Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Manifold learning techniques and model reduction applied to dissipative
  PDEs   We link nonlinear manifold learning techniques for data analysis/compression
with model reduction techniques for evolution equations with time scale
separation. In particular, we demonstrate a `"nonlinear extension" of the
POD-Galerkin approach to obtaining reduced dynamic models of dissipative
evolution equations. The approach is illustrated through a reaction-diffusion
PDE, and the performance of different simulators on the full and the reduced
models is compared. We also discuss the relation of this nonlinear extension
with the so-called "nonlinear Galerkin" methods developed in the context of
Approximate Inertial Manifolds.

10
The categories of this paper are: q-bio
5
The categories of this paper are: gr-qc
7
The categories of this paper are: hep-th
37
The categories of this paper are: supr-con
8
The categories of this paper are: hep-ex
4
The categories of this paper are: cond-mat
19
The categories of this paper are: eess
32
The categories of this paper are: atom-ph
0
The categories of this paper are: hep-ph
33
The categories of this paper are: plasm-ph
29
The categories of this paper are: funct-an
16
The categories of this paper are: stat
30
The categories of this paper are: q-alg
22
The categories of this paper are: chao-dyn
36
The categories of this paper are: cmp-lg
24
The categories of this paper are: dg-ga