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Differential equation discovery, a machine learning subfield, is used todevelop interpretable models, particularly in nature-related applications. Byexpertly incorporating the general parametric form of the equation of motionand appropriate differential terms, algorithms can autonomously uncoverequations from data. This paper explores the prerequisites and tools forindependent equation discovery without expert input, eliminating the need forequation form assumptions. We focus on addressing the challenge of assessingthe adequacy of discovered equations when the correct equation is unknown, withthe aim of providing insights for reliable equation discovery without priorknowledge of the equation form.

Towards true discovery of the differential equations