  This research focuses on an innovative task of extracting equations from
incomplete data, moving away from traditional methods used for complete
solutions. The study addresses the challenge of extracting equations from data,
particularly in the study of brain activity using electrophysiological data,
which is often limited by insufficient information. The study provides a brief
review of existing open-source equation derivation approaches in the context of
modeling brain activity. The section below introduces a novel algorithm that
employs incomplete data and prior domain knowledge to recover differential
equations. The algorithm's practicality in real-world scenarios is demonstrated
through its application on both synthetic and real datasets.
