- MAE Home
- About MAE
- Academic Programs
- Faculty & Staff
Areas of Interest
- Fluid dynamics
- Turbulence modeling and large eddy simulations
- Geophysical flows
- Multiphase and multimaterial flows
- High performance computing
- Model reduction and optimization
- Computational mathematics and numerical methods
- Data assimilation
- Machine learning and data-driven modeling
- Digital twin technologies
Ph.D., Engineering Mechanics, Virginia Tech, 2012
M.S., Aerospace Engineering, Old Dominion University, 2007
B.S., Aeronautical Engineering, Istanbul Technical University, 2005
- Assistant Professor, School of Mechanical and Aerospace Engineering, Oklahoma State University, 2015-present
- Postdoctoral Associate, Center for Shock Wave-processing of Advanced Reactive Materials, University of Notre Dame, 2014-2015
- Postdoctoral Associate, Interdisciplinary Center for Applied Mathematics, Virginia Tech, 2012-2014
Engineering Analysis and Methods
Engineering Numerical Analysis
Data Assimilation in Science and Engineering
Honors and Awards
CEAT Excellent Scholar Award, Oklahoma State University, 2020
U.S. Department of Energy Early Career Research Program Award in Applied Mathematics, 2018
Ahmed, S. E., San, O., Rasheed, A. and Iliescu, T. A long short-term memory embedding for hybrid uplifted reduced order models. Physica D: Nonlinear Phenomena, 409, 132471, 2020.
Pawar, S., Ahmed, S. E., San, O. and Rasheed, A. Data-driven recovery of hidden physics in reduced order modeling of fluid flows. Physics of Fluids, 32, 036602, 2020.
Vaddireddy, H., Rasheed, A., Staples, A. E. and San, O. Feature engineering and symbolic regression methods for detecting hidden physics from sparse sensor observation data. Physics of Fluids, 32, 015113, 2020.
Rasheed, A., San, O. and Kvamsdal, T. Digital twin: values, challenges and enablers from a modeling perspective. IEEE Access, 8, 21980-22012, 2020.
Rahman, S. M., Pawar, S., San, O., Rasheed, A. and Iliescu, T. Nonintrusive reduced order modeling framework for quasigeostrophic turbulence. Physical Review E, 100, 053306, 2019.
Maulik, R., San, O., Jacob, J. and Crick, C. Sub-grid scale model classification and blending through deep learning. Journal of Fluid Mechanics, 870, 784-812, 2019.
Maulik, R., San, O., Rasheed, A. and Vedula, P. Sub-grid modelling for two-dimensional turbulence using neural networks. Journal of Fluid Mechanics, 858, 122-144, 2019.
San, O. and Maulik, R. Stratified Kelvin–Helmholtz turbulence of compressible shear flows. Nonlinear Processes in Geophysics, 25, 457-476, 2018.
San, O. and Maulik, R. Extreme learning machine for reduced order modeling of turbulent geophysical flows. Physical Review E, 97, 042322, 2018.
San, O. and Maulik, R. Neural network closures for nonlinear model order reduction. Advances in Computational Mathematics, 44(6), 1717-1750, 2018.
San, O. and Vedula, P. Generalized deconvolution procedure for structural modeling of turbulence. Journal of Scientific Computing, 75(2), 1187-1206, 2018.
Maulik, R. and San, O. A neural network approach for the blind deconvolution of turbulent flows. Journal of Fluid Mechanics, 831, 151-181, 2017.
San, O. and Borggaard, J. Principal interval decomposition framework for POD reduced-order modeling of convective Boussinesq flows. International Journal for Numerical Methods in Fluids, 78, 37-62, 2015.
San, O., Staples, A. E. and Iliescu, T. Approximate deconvolution large eddy simulation of a stratified two-layer quasigeostrophic ocean model. Ocean Modelling, 63, 1-20, 2013.
San, O. and Staples, A. E. A coarse-grid projection method for accelerating incompressible flow computations. Journal of Computational Physics, 233, 480-508, 2013.