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In this paper, we present a machine learning method for the discovery ofanalytic solutions to differential equations. The method utilizes an inherentlyinterpretable algorithm, genetic programming based symbolic regression. Unlikeconventional accuracy measures in machine learning we demonstrate the abilityto recover true analytic solutions, as opposed to a numerical approximation.The method is verified by assessing its ability to recover known analyticsolutions for two separate differential equations. The developed method iscompared to a conventional, purely data-driven genetic programming basedsymbolic regression algorithm. The reliability of successful evolution of thetrue solution, or an algebraic equivalent, is demonstrated.

Genetic Programming Based Symbolic Regression for Analytical Solutions to Differential Equations