11 citations · 21 across the 4 of their papers we have counts for
4 papers
A Lagrangian Conditional Gaussian Koopman Network for Data Assimilation and Prediction
Zhongrui Wang, Chuanqi Chen, Jin-Long Wu +1
Lagrangian data assimilation aims to recover hidden Eulerian flow fields from sparse, indirect observations of moving tracers. This problem is challenging because tracer trajectori…
CGKN: A Deep Learning Framework for Modeling Complex Dynamical Systems and Efficient Data Assimilation
Chuanqi Chen, Nan Chen, Yinling Zhang +1
Deep learning is widely used to predict complex dynamical systems in many scientific and engineering areas. However, the black-box nature of these deep learning models presents sig…
CGNSDE: Conditional Gaussian Neural Stochastic Differential Equation for Modeling Complex Systems and Data Assimilation
Chuanqi Chen, Nan Chen, Jin-Long Wu
A new knowledge-based and machine learning hybrid modeling approach, called conditional Gaussian neural stochastic differential equation (CGNSDE), is developed to facilitate modeli…
A Physics-Informed Auto-Learning Framework for Developing Stochastic Conceptual Models for ENSO Diversity
Yinling Zhang, Nan Chen, Jerome Vialard +1
Understanding ENSO dynamics has tremendously improved over the past decades. However, one aspect still poorly understood or represented in conceptual models is the ENSO diversity i…