  Differential equation discovery, a machine learning subfield, is used to
develop interpretable models, particularly in nature-related applications. By
expertly incorporating the general parametric form of the equation of motion
and appropriate differential terms, algorithms can autonomously uncover
equations from data. This paper explores the prerequisites and tools for
independent equation discovery without expert input, eliminating the need for
equation form assumptions. We focus on addressing the challenge of assessing
the adequacy of discovered equations when the correct equation is unknown, with
the aim of providing insights for reliable equation discovery without prior
knowledge of the equation form.
