My research lies at the interface of applied and computational mathematics, machine learning, and geophysics. 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 motivated by geophysical problems, such as recovering ocean flow fields from sparse Lagrangian trajectory observations, weather and climate prediction, and ENSO.

Themes: nonlinear & non-Gaussian DAensemble DAmachine learningsurrogate modelingLagrangian DADA theory

Ongoing Work

A Physics-Embedded Conditional Gaussian Koopman Network as a Digital Twin of ENSO with Closed-Form Data Assimilation
Zhongrui Wang and Nan Chen
In preparation, 2026 machine learningsurrogate modeling

Discrete- and Continuous-Time Data Assimilation: The Role of Observational Noise and Bridges
Zhongrui Wang and Nan Chen
In preparation, 2026 nonlinear & non-Gaussian DADA theory

Preprints

A Regime-Dependent Multi-Model Framework for Data Assimilation and Prediction   [poster]
Zhongrui Wang, Rebecca Willett, Nan Chen, and Yinling Zhang
In preparation, 2026 nonlinear & non-Gaussian DAensemble DAmachine learningsurrogate modeling

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 nonlinear & non-Gaussian DAmachine learningsurrogate modelingLagrangian DA

Selected Publications

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 nonlinear & non-Gaussian DAmachine learningsurrogate modeling

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 nonlinear & non-Gaussian DALagrangian DA

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 ensemble DAmachine learning

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 ensemble DA

Integrated Hybrid Data Assimilation for an Ensemble Kalman Filter
Lili Lei, Zhongrui Wang, and Zhe-Min Tan
Monthly Weather Review (MWR), 2021 ensemble DA

* Denotes joint authorship.

Academic Service

Reviewer, Physica D: Nonlinear Phenomena, 2026
Reviewer, npj Artificial Intelligence, 2025