Changhong Mou
Mathematics and Statistics
Assistant Professor
Biography
Before joining USU, I was a postdoctoral scholar in the Department of Mathematics at Purdue University (Fall 2024–2025) and a Van Vleck Visiting Assistant Professor at the University of Wisconsin–Madison (Fall 2021–2024). I earned my Ph.D. in Mathematics from Virginia Tech under the supervision of Prof. Traian Iliescu.
Personal Webpage: https://sites.google.com/view/cmou/home
Teaching Interests
My teaching interests are in applied mathematics and scientific computing, including numerical analysis, differential equations, linear algebra, optimization, and stochastic methods. I also enjoy teaching data-driven modeling and scientific machine learning, with an emphasis on linking mathematical foundations to practical computation through coding and project-based learning.
Research Interests
*Reduced Order Modeling (ROM)
*Data Assimilation (DA)
*Scientific Machine Learning (SciML)
*Stochastic Modeling
*Numerical Analysis (NA)
- Snyder, W., Mou, C., Liu, H., San, O., DeVita, R., Iliescu, T., (2022). Reduced Order Model Closures: A Brief Tutorial: Advances in Mathematical Fluid Mechanics. Springer International Publishing
Publications | Book Chapters
An asterisk (*) at the end of a publication indicates that it has not been peer-reviewed.
Publications | Journal Articles
Academic Journal
- Lu, B., Mou, C., Lin, G., (2026). iPINNER: An iterative physics-informed neural network with ensemble Kalman filter. Journal of Computational Physics, 548, 114592. doi: 10.1016/j.jcp.2025.114592
- Lin, G., Mou, C., Zhang, J., (2025). Energy-dissipative evolutionary Kolmogorov-Arnold networks for complex PDE systems. Journal of Computational Physics, doi: 10.1016/j.jcp.2025.114326
- Mou, C., Samuel Stechmann, 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, doi: 10.5194/npg-32-329-2025
- Chen, N., Mou, C., Smith, L.M, Zhang, Y., (2024). A stochastic precipitating quasi-geostrophic model. Physics of Fluids, 36:11, doi: 10.1063/5.0231366
- Mou, C., Chen, N., Iliescu, T., (2023). An efficient data-driven multiscale stochastic reduced order modeling framework for complex systems. Journal of Computational Physics, 493, doi: 10.1016/j.jcp.2023.112450
- 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. doi: 10.1016/j.nucengdes.2023.112454
- 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, doi: 10.1029/2022MS003597
- Snyder, W., McGuire, J.A, Mou, C., Dillard, D.A, Iliescu, T., De Vita, R., (2023). Data-driven variational multiscale reduced order modeling of vaginal tissue inflation. International Journal for Numerical Methods in Biomedical Engineering, 39:1, doi: 10.1002/cnm.3660
- 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, doi: 10.1007/s10915-022-02019-y
- 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. doi: 10.1016/j.cma.2020.113470
- 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. doi: 10.1137/20M1349965
- Mou, C., Wang, Z., Wells, D.R, Xie, X., Iliescu, T., (2020). Reduced Order Models for the Quasi-Geostrophic Equations: A Brief Survey. Fluids, 6:1, 16. doi: 10.3390/fluids6010016
- 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. doi: 10.3390/fluids5040189
- 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. doi: 10.1080/10618562.2020.1723556
- Koc, B., Mohebujjaman, M., Mou, C., Iliescu, T., (2019). Commutation error in reduced order modeling of fluid flows. Advances in Computational Mathematics, 45:5-6, 2587-2621. doi: 10.1007/s10444-019-09739-0
An asterisk (*) at the end of a publication indicates that it has not been peer-reviewed.
Publications | Other
Other
- Mou, C., Merzari, E., San, O., Iliescu, T., (2021). 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)
An asterisk (*) at the end of a publication indicates that it has not been peer-reviewed.