My research lies at the interface of applied and computational mathematics, statistics, and machine learning. I focus on developing analytically accurate yet computationally tractable data assimilation and scientific machine learning for complex dynamical systems -- nonlinear, chaotic, high-dimensional, and usually observed only in part. Much of this work is driven by geophysical problems, such as recovering ocean flow fields from sparse Lagrangian trajectory observations.
Themes: nonlinear & non-Gaussian DAensemble DAmachine learningsurrogate modelingLagrangian DA
A Lagrangian Conditional Gaussian Koopman Network for Data Assimilation and Prediction [slides] [poster] [code]
Zhongrui Wang, Chuanqi Chen, Jin-Long Wu, and Nan Chen
Arxiv, 2026
Modeling partially observed nonlinear dynamical systems and efficient data assimilation via discrete-time conditional Gaussian Koopman network [slides] [poster] [code]
Chuanqi Chen, Zhongrui Wang, Nan Chen, and Jin-Long Wu
Computer Methods in Applied Mechanics and Engineering, 2025
A Nonlinear Data Assimilation Algorithm with Closed-Form Approximations for Multi-Layer Flow Fields [slides] [poster] [code]
Zhongrui Wang, Nan Chen, and Di Qi
Monthly Weather Review (MWR), 2025
CNN-based adaptive localization for an ensemble Kalman filter [code]
Zhongrui Wang, Lili Lei, Jeffrey L. Anderson, Zhe-Min Tan, and Yi Zhang
Journal of Advances in Modeling Earth Systems (JAMES), 2023
The importance of data assimilation components for initial conditions and subsequent error growth
Zhongrui Wang*, Haohao Sun*, Lili Lei, and Zhe-Min Tan
Science China Earth Sciences, 2024
Integrated Hybrid Data Assimilation for an Ensemble Kalman Filter
Lili Lei, Zhongrui Wang, and Zhe-Min Tan
Monthly Weather Review (MWR), 2021
Reviewer, Physica D: Nonlinear Phenomena, 2026
Reviewer, npj Artificial Intelligence, 2025