abstract = "Partial differential equations provide accurate models
for many physical processes, although their derivation
can be challenging, requiring a fundamental
understanding of the modeled system. This challenge can
be circumvented with the data-driven algorithms that
obtain the governing equation only using observational
data. One of the tools commonly used in search of the
differential equation is the evolutionary optimization
algorithm. we seek to improve the existing evolutionary
approach to data-driven partial differential equation
discovery by introducing a more reliable method of
evaluating the quality of proposed structures, based on
the inclusion of the automated algorithm of partial
differential equations solving. In terms of
evolutionary algorithms, we want to check whether the
more computationally challenging fitness function
represented by the equation solver gives the sufficient
resulting solution quality increase with respect to the
more simple one. The approach includes a
computationally expensive equation solver compared with
the baseline method, which used equation discrepancy to
define the fitness function for a candidate structure
in terms of algorithm convergence and required
computational resources on the synthetic data obtained
from the solution of the Korteweg-de Vries equation.",