It is an implementation of "Symbolic genetic algorithm for discovering open-form partial differential equations (SGA-PDE)" by the original author. The official repository is available [here] and the published paper is [here].
This algorithm can efficiently establish mathematical equation with tree representation to fit existing dataset. We can discover partial derivative equation (PDE) with nested forms without any prior knowledge about the equation candidate terms. The genetic algorithm provides exploration and optimization on symbolic mathematics, the forest provides a concise way to represent equations, and machine learning provides a fitting accuracy indicator.
Version: This repo is an early version that contains the basic structure of the algorithm/code. For re-implementing the paper, please see the official repository.