PUBLICATIONS
(*-- Correspondence, †--Student mentored.)
PUBLICATIONS
(*-- Correspondence, †--Student mentored.)
In Preparation
Mou, C., Lu, B.†, & Lin, G. (2026). Neural-POD: A Neural Operator Approach for Infinite-Dimensional Functional Nonlinear Proper Orthogonal Decomposition. In preparation.
Mou, C., Liu, H., Rebholz, L. G., & Iliescu, T. (2023). Hybrid Data-Driven Reduced Order Models for 3D Channel Flows. In preparation.
Refereed Journal and Conference Articles:
Submitted
Lu, B.†, Mou, C., & Lin, G. (2025). Morephy-Net: An Evolutionary Multi-objective Optimization for Replica-Exchange-based Physics-informed Operator Learning Network. Submitted; preprint, arXiv:2509.00663.
Mi, H., Lun, H., Mou, C., & Zhang, Y. (2025). PIP^2 Net: Physics-Informed Partition Penalty Deep Operator Network. Submitted; preprint, arXiv:2512.15086.
Mou, C., Zhang, Y., Zhu, X., & Zhuang, Q. (2025). PAS-Net: Physics Informed Adaptive Scale Deep Operator Network. Submitted; preprint, arXiv:2511.14925.
Park, D., Mou, C., Liu, H., Sandu, A., & Iliescu, T. (2022). A Two-Level Galerkin Reduced Order Model for the Steady Navier-Stokes Equations. Submitted; preprint, arXiv:2211.12968.
Lu, F., Mou, C., Liu, H., & Iliescu, T. (2022).Stochastic Data-Driven Variational Multiscale Reduced Order Models. Submitted; preprint, arXiv:2209.0273.
Published
Lu, B.†, Mou, C., & Lin, G. (2025). iPINNER: An Iterative Physics-Informed Neural Network with Ensemble Kalman Filter. Journal of Computational Physics, 114592.
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.
Lin, G., Mou, C., & Zhang, J. (2025). Energy-Dissipative Evolutionary Kolmogorov-Arnold Networks for Complex PDE Systems. Journal of Computational Physics, 114326.
Chen, N., Mou, C.*, Smith, L. M., & Zhang, Y. (2024). A Stochastic Precipitating Quasi-Geostrophic Model. Physics of Fluids, 36, 116618.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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
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
Mou, C. (2021). Data-Driven Variational Multiscale Reduced Order Modeling of Turbulent Flows (Doctoral dissertation, Virginia Tech).
Mou, C. (2018). Cross-validation of data-driven correction reduced order modeling (Master Thesis, Virginia Tech).