Toward a physics-guided machine learning approach for predicting chaotic systems dynamics.

ABSTRACT: Predicting the dynamics of chaotic systems is crucial across various practical domains, including the control of infectious diseases and responses to extreme weather events. Such predictions provide quantitative insights into the future behaviors of these complex systems, thereby guiding the decision-making and planning within the respective fields. Recently, data-driven approaches, renowned for their capacity to learn from empirical data, have been widely used to predict chaotic system dynamics. However, these methods rely solely on historical observations while ignoring the underlying mechanisms that govern the systems' behaviors. Consequently, they may perform well in short-term predictions by effectively fitting the data, but their ability to make accurate long-term predictions is limi

SUBMITTER: Feng L 

PROVIDER: S-EPMC11782262 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature