Genetic Programming Based Symbolic Regression for Analytical Solutions to Differential Equations
Abstract
In this paper, we present a machine learning method for the discovery of analytic solutions to differential equations. The method utilizes an inherently interpretable algorithm, genetic programming based symbolic regression. Unlike conventional accuracy measures in machine learning we demonstrate the ability to recover true analytic solutions, as opposed to a numerical approximation. The method is verified by assessing its ability to recover known analytic solutions for two separate differential equations. The developed method is compared to a conventional, purely data-driven genetic programming based symbolic regression algorithm. The reliability of successful evolution of the true solution, or an algebraic equivalent, is demonstrated.
Keywords
Cite
@article{arxiv.2302.03175,
title = {Genetic Programming Based Symbolic Regression for Analytical Solutions to Differential Equations},
author = {Hongsup Oh and Roman Amici and Geoffrey Bomarito and Shandian Zhe and Robert Kirby and Jacob Hochhalter},
journal= {arXiv preprint arXiv:2302.03175},
year = {2023}
}
Comments
14 pages, 9 figures