The field of algorithmic search for physical models is relatively new, but has seen a number of important advances. Recently, Brunton et al. introduced a sparse regression approach to discover equations governing the physics in a chaotic Lorenz system, and in a fluid vortex dynamic system . However, this technique is restricted to a pre-defined algebraic model structure, as selected basis functions are linearly combined. Allowing free-form analytical equations, Bongard and Lipson developed a criterion to find meaningful and complex mathematically invariant models by means of the ML method of symbolic regression (SR).