  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.
