Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Data-driven PDE discovery with evolutionary approach   The data-driven models allow one to define the model structure in cases when
a priori information is not sufficient to build other types of models. The
possible way to obtain physical interpretation is the data-driven differential
equation discovery techniques. The existing methods of PDE (partial derivative
equations) discovery are bound with the sparse regression. However, sparse
regression is restricting the resulting model form, since the terms for PDE are
defined before regression. The evolutionary approach described in the article
has a symbolic regression as the background instead and thus has fewer
restrictions on the PDE form. The evolutionary method of PDE discovery (EPDE)
is described and tested on several canonical PDEs. The question of robustness
is examined on a noised data example.

10
The categories of this paper are: q-bio
6
The categories of this paper are: astro-ph
0
The categories of this paper are: hep-ph
18
The categories of this paper are: econ
3
The categories of this paper are: physics
28
The categories of this paper are: alg-geom
33
The categories of this paper are: plasm-ph
19
The categories of this paper are: eess
14
The categories of this paper are: math-ph
9
The categories of this paper are: nlin
13
The categories of this paper are: quant-ph
26
The categories of this paper are: bayes-an
11
The categories of this paper are: hep-lat
16
The categories of this paper are: stat
31
The categories of this paper are: ao-sci
36
The categories of this paper are: cmp-lg