Refereed Journal and Conference Articles:

Submitted 

1.  Mou, C. (2026). Stochastic Physics-Constrained Operator Inference for Complex Systems. arXiv preprint arXiv:2609.33380.

2.  Xu, J., Mou, C., Liu, K., Xu, D., & Zhang, Y. (2026). Geometric Identification in Predict-Then-Optimize Learning. arXiv preprint arXiv:2609.33472.

3.  Feng, J., Hu, B., Jiang, Y., Mou, C., & Zhang, Y. (2026). Finance-Informed Operator Learning for Option Pricing with Quantum-Compatible Realizations. arXiv preprint arXiv:2609.33213.

4.  Zhuang, Q., Mou, C., Liu, B., Fu, M.R. and King, G.W. (2026). REINA: A Recognize-Then-Infer Wearable-to-App AI Framework for Breast Cancer Rehabilitation.  Submitted; preprint medRxiv, pp.2026-08.

5.  Deng, Z., Lu, B., Feng, H., Huang, S., Wang, D., Xu, Y., Zhang, Z., Sun, Y., Mou, C., Zhang, R. and Hao, Y.  (2026). CONTRAMEM: Learning Self-Evolving Procedural Memory from Contrasting Multi-Model Trajectories. Submitted; preprint arXiv:2608.22533.

6.  Afful, B. A., Mou, C., & Gordillo, L. (2026). Hybrid SINDy-EnKF in Learning Chikungunya Dynamics from Incomplete, Noisy or Partially Observed Data. Submitted; preprint arXiv:2607.27137.

7.  Mou, C., Xu, D., Zuo, X., Liu, K., & Zhang, Y. (2026). ATLAS-NN: Adaptive Transfer Learnable Symplectic-aware Neural Network for Long-Time Hamiltonian Dynamics. Submitted; preprint, arXiv:2606.04447.

8.  Lu, B.†, Zhang, R., Mou, C., Li, N. and Lin, G. (2026). AdamFLIP: Adaptive Momentum Feedback Linearization Optimization for Hard Constrained PINN Training. Submitted; preprint, arXiv:2605.08408.

9.  Xu, J., Mou, C., Zhang, Y., & He, F. (2026). Physics-Aligned Canonical Equivariant Fourier Neural Operator under Symmetry-Induced Shifts. Submitted; preprint, arXiv:2605.18606.

10.  Lu, B.†, Deng, Z., Zhang, R., Hu, B., Zhao, Y., Tian, Y., Mou, C., Lin, G. and Li, X. (2026). Muon-OGD: Muon-based Spectral Orthogonal Gradient Projection for LLM Continual Learning. Submitted; preprint, arXiv:2605.08949.

11.  Mou, C., Lu, B.†, & Lin, G. (2026). Neural-POD: A Plug-and-Play Neural Operator Framework for Infinite-Dimensional Functional Nonlinear Proper Orthogonal Decomposition. Submitted; preprint, arXiv: 2602.15632.

12.  Mou, C., Zhang, Y., Zhu, X., & Zhuang, Q. (2025). PAS-Net: Physics Informed Adaptive Scale Deep Operator Network. Submitted; preprint, arXiv:2511.14925. 

13.  Lu, F., Mou, C., Liu, H., & Iliescu, T. (2022).Stochastic Data-Driven Variational Multiscale Reduced Order Models. Submitted; preprint, arXiv:2209.0273.

Published 

14.   Park, D., Mou, C., Liu, H., Sandu, A., & Iliescu, T. (2026). A Two-Level Galerkin Reduced Order Model for the Steady Navier-Stokes Equations. Accepted to Numerical Methods for Partial Differential Equations; preprint, arXiv:2211.12968. 

15.  Lu, B.†, Mou, C., & Lin, G. (2026). Morephy-Net: An Evolutionary Multi-objective Optimization for Replica-Exchange-based Physics-informed Operator Learning Network. Computer Methods in Applied Mechanics and Engineering, 460, 119123.

16.  Mi, H., Lun, H., Mou, C., & Zhang, Y. (2026). PIP^2 Net: Physics-Informed Partition Penalty Deep Operator Network. Electronic Research Archive, 34(3), 2009–2037. https://doi.org/10.3934/era.2026090.  

17.  Lu, B.†, Mou, C., & Lin, G. (2025). iPINNER: An Iterative Physics-Informed Neural Network with Ensemble Kalman Filter. Journal of Computational Physics, 114592.

18.  Mou, C., Stechmann, S. N., & Chen, N. (2025). Simulation and data assimilation in an idealized coupled atmosphere–ocean–sea ice floe model with cloud effects. Nonlinear Processes in Geophysics, 32(3), 329–351.

19.  Lin, G., Mou, C., & Zhang, J. (2025). Energy-Dissipative Evolutionary Kolmogorov-Arnold Networks for Complex PDE Systems. Journal of Computational Physics, 114326. 

20.  Chen, N., Mou, C.*, Smith, L. M., & Zhang, Y. (2024). A Stochastic Precipitating Quasi-Geostrophic Model. Physics of Fluids, 36, 116618. 

21.  Mou, C., Smith, L. M., & Chen, N. (2023). Combining Stochastic Parameterized Reduced-Order Models with Machine Learning for Data Assimilation and Uncertainty Quantification with Partial Observations. Journal of Advances in Modeling Earth Systems, 15(10), e2022MS003597.

22.  Mou, C., Chen, N., & Iliescu, T. (2023). An Efficient Data-Driven Multiscale Stochastic Reduced Order Modeling Framework for Complex Systems. Journal of Computational Physics, 112450.  

23.  Mou, C., Merzari, E., San, O., & Iliescu, T. (2023). An energy-based lengthscale for reduced order models of turbulent flows. Nuclear Engineering and Design, 412, 112454.

24.  Snyder, W., McGuire, J. A., Mou, C., Dillard, D. A., Iliescu, T., & De Vita, R. (2022). Data‐Driven Variational Multiscale Reduced Order Modeling of Vaginal Tissue Inflation. International Journal for Numerical Methods in Biomedical Engineering, e3660.

25.  Koc, B., Mou, C., Liu, H., Wang, Z., Rozza, G., & Iliescu, T. (2022). Verifiability of the data-driven variational multiscale reduced order model. Journal of Scientific Computing, 93(2), 1-26.

26.  Mou, C., Merzari, E., San, O., & Iliescu, T. (2022). A numerical investigation of the lengthscale in the mixing-length reduced order model of the turbulent channel flow. 19th International Topical Meeting on Nuclear Reactor Thermal Hydraulics (NURETH-19), Brussels, Belgium, 2022.

27.  Mou, C., Wang, Z., Wells, D. R., Xie, X., & Iliescu, T. (2021). Reduced Order Models for the Quasi-Geostrophic Equations: A Brief Survey. Fluids, 6(1), 16.

28.  Popov, A. A., Mou, C., Sandu, A., & Iliescu, T. (2021). A Multifidelity Ensemble Kalman Filter with Reduced Order Control Variates. SIAM Journal on Scientific Computing, 43(2), A1134-A1162.

29.  Mou, C., Koc, B., San, O., Rebholz, L. G., & Iliescu, T. (2021). Data-Driven Variational Multiscale Reduced Order Models. Computer Methods in Applied Mechanics and Engineering, 373, 113470.

30.  Xie, X., Nolan, P. J., Ross, S. D., Mou, C., & Iliescu, T. (2020). Lagrangian Reduced Order Modeling Using Finite Time Lyapunov Exponents. Fluids, 5(4), 189.

31.  Mou, C., Liu, H., Wells, D. R., & Iliescu, T. (2020). Data-Driven Correction Reduced Order Models for the Quasi-Geostrophic Equations: A Numerical Investigation. International Journal of Computational Fluid Dynamics, 34(2), 147-159.

32.  Koc, B., Mohebujjaman, M., Mou, C., & Iliescu, T. (2019). Commutation Error in Reduced Order Modeling of Fluid Flows. Advances in Computational Mathematics, 45(5), 2587-2621.

Book Chapters

33.  Snyder, W., Mou, C., Liu, H., San, O., DeVita, R., & Iliescu, T. (2022). Reduced order model closures: A brief tutorial. Recent Advances in Mechanics and Fluid-Structure Interaction with Applications (pp. 167-193). Birkhäuser, Cham.

Thesis & Other Writings

34.  Mou, C. (2021). Data-Driven Variational Multiscale Reduced Order Modeling of Turbulent Flows (Doctoral dissertation, Virginia Tech).

35.  Mou, C. (2018). Cross-validation of data-driven correction reduced order modeling (Master Thesis, Virginia Tech).