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.

该研究针对从不完全数据中提取方程的创新任务，摆脱传统用于完整解决方案的方法，解决了从数据中提取方程的挑战，特别是在使用电生理数据研究脑活动时，通常受到不充分信息的限制。该研究在建模脑活动的背景下，对现有的开源方程推导方法进行了简要回顾。接下来的部分介绍了一种新的算法，该算法利用不完全数据和先前的领域知识来恢复微分方程，并通过在合成数据集和真实数据集上的应用展示了该算法在现实世界场景中的实用性。