Towards true discovery of the differential equations

arXiv:2308.04901v2 Announce Type: replace
Abstract: 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 …

abstract algorithms applications arxiv cs.lg data differential differential equation discovery equation form general machine machine learning nature paper parametric terms tools true type