Interpretable predictions of chaotic dynamical systems using dynamical system deep learning.

ABSTRACT: Making accurate predictions of chaotic dynamical systems is an essential but challenging task with many practical applications in various disciplines. However, the current dynamical methods can only provide short-term precise predictions, while prevailing deep learning techniques with better performances always suffer from model complexity and interpretability. Here, we propose a new dynamic-based deep learning method, namely the dynamical system deep learning (DSDL), to achieve interpretable long-term precise predictions by the combination of nonlinear dynamics theory and deep learning methods. As validated by four chaotic dynamical systems with different complexities, the DSDL framework significantly outperforms other dynamical and deep learning methods. Furthermore, the DSDL also reduce

SUBMITTER: Wang M 

PROVIDER: S-EPMC10850482 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature