Omer San | Mechanical, Aerospace and Biomedical Engineering

2023-present
Associate Professor, Department of Mechanical, Aerospace and Biomedical Engineering, University of Tennessee, Knoxville, TN

2021-2023
Associate Professor, School of Mechanical and Aerospace Engineering
Oklahoma State University, Stillwater, OK

2015-2021
Assistant Professor, School of Mechanical and Aerospace Engineering
Oklahoma State University, Stillwater, OK

2014-2015
Postdoctoral Researcher, Department of Aerospace and Mechanical Engineering
University of Notre Dame, Notre Dame, IN

2012-2014
Postdoctoral Researcher, Interdisciplinary Center for Applied Mathematics
Virginia Tech, Blacksburg, VA

PhD, 2012, Engineering Mechanics, Virginia Tech, Blacksburg, VA

MS, 2007, Aerospace Engineering, Old Dominion University, Norfolk, VA

BS, 2005, Aeronautical Engineering, Istanbul Technical University, Turkey

Member of APS, AIAA, SIAM.
Scientific reviewer for manuscripts from leading journals, and handling editor for several special issues on reduced order modeling, computational fluid dynamics, and machine learning.
Academic editor of PLoS ONE specialized in oceanography, applied mathematics, artificial intelligence, and data science.
Organized of professional conferences, minisymposiums and chaired sessions at scientific meetings, and served on technical committees.
Served on evaluation panels of DOD, DOE, and NSF programs as well as for international programs including Norway’s RCN, as well as other schemes from Canada's NSERC, France's PRCE, and Netherlands' NWO.

Ahmed, S. E., San, O., Lakshmivarahan, S. and Lewis, J. M. On the dual advantage of placing observations through forward sensitivity analysis. Proceedings of the Royal Society A, 479, 20220815, 2023.
DOI: https://doi.org/10.1098/rspa.2022.0815

Ahmed, S. E., San, O., Rasheed, A., Iliescu, T. and Veneziani A. Physics guided machine learning for variational multiscale reduced order modeling. SIAM Journal on Scientific Computing, 45, B283-B313, 2023.
DOI: https://doi.org/10.1137/22M1496360

Blakseth, S. S., Rasheed, A., Kvamsdal, T. and San, O. Combining physics-based and data-driven techniques for reliable hybrid analysis and modeling using the corrective source term approach. Applied Soft Computing, 128:109533, 2022. DOI: https://doi.org/10.1016/j.asoc.2022.109533

Pawar, S. and San, O. Equation-free surrogate modeling of geophysical flows at the intersection of machine learning and data assimilation. Journal of Advances in Modeling Earth Systems, 14:e2022MS003170, 2022.
DOI: https://doi.org/10.1029/2022MS003170

Pawar, S., San, O., Rasheed, A. and Vedula, P. Frame invariant neural network closures for Kraichnan turbulence. Physica A: Statistical Mechanics and its Applications, 609:128327, 2022.
DOI: https://doi.org/10.1016/j.physa.2022.128327

Pawar, S. and San, O. Data assimilation empowered neural network parametrizations for subgrid processes in geophysical flows. Physical Review Fluids, 6(5):050501, 2021.
DOI: https://doi.org/10.1103/PhysRevFluids.6.050501

Ahmed, S. E., Pawar, S., San, O., Rasheed, A., Iliescu, T. and Noack, B. R. On closures for reduced order models – A spectrum of first-principle to machine-learned avenues. Physics of Fluids, 33, 091301, 2021.
DOI: https://doi.org/10.1063/5.0061577

Rasheed, A., San, O. and Kvamsdal, T. Digital twin: values, challenges and enablers from a modeling perspective. IEEE Access, 8, 21980-22012, 2020.
DOI: https://doi.org/10.1109/ACCESS.2020.2970143

Maulik, R., San, O., Rasheed, A., and Vedula, P. Subgrid modelling for two-dimensional turbulence using neural networks. Journal of Fluid Mechanics, 858:122–144, 2019. DOI: https://doi.org/10.1017/jfm.2018.770

San, O. and Maulik, R. Extreme learning machine for reduced order modeling of turbulent geophysical flows. Physical Review E, 97(4):042322, 2018.
DOI: https://doi.org/10.1103/PhysRevE.97.042322