Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Towards an Adaptive Dynamic Mode Decomposition   Dynamic Mode Decomposition (DMD) is a data based modeling tool that
identifies a matrix to map a quantity at some time instant to the same quantity
in future. We design a new version which we call Adaptive Dynamic Mode
Decomposition (ADMD) that utilizes time delay coordinates, projection methods
and filters as per the nature of the data to create a model for the available
problem. Filters are very effective in reducing the rank of high-dimensional
dataset. We have incorporated 'discrete Fourier transform' and 'augmented
lagrangian multiplier' as filters in our method. The proposed ADMD is tested on
several datasets of varying complexities and its performance appears to be
promising.

9
The categories of this paper are: nlin
15
The categories of this paper are: nucl-ex
0
The categories of this paper are: hep-ph
20
The categories of this paper are: acc-phys
37
The categories of this paper are: supr-con
26
The categories of this paper are: bayes-an
23
The categories of this paper are: solv-int
16
The categories of this paper are: stat
14
The categories of this paper are: math-ph
28
The categories of this paper are: alg-geom
31
The categories of this paper are: ao-sci
7
The categories of this paper are: hep-th
8
The categories of this paper are: hep-ex
5
The categories of this paper are: gr-qc
29
The categories of this paper are: funct-an
13
The categories of this paper are: quant-ph