One thing I have learned in a long life: that all our science, measured against reality, is primitive and childlike — and yet it is the most precious thing we have.

Albert Einstein

Please see Omer San's Google Scholar profile for a current list of publications.  A list of arXiv preprints can be found here.

Selected Publications

Variational multiscale reinforcement learning for discovering reduced order closure models of nonlinear spatiotemporal transport systems

San, O., Pawar, S. and Rasheed, A. Scientific Reports, 12, 17947, 2022. [arXiv]

Equation-free surrogate modeling of geophysical flows at the intersection of machine learning and data assimilation

Pawar, S. and San, O. Journal of Advances in Modeling Earth Systems, 14:e2022MS003170, 2022. [arXiv]

Combining physics-based and data-driven techniques for reliable hybrid analysis and modeling using the corrective source term approach

Blakseth, S. S., Rasheed, A., Kvamsdal, T. and San, O. Applied Soft Computing, 128, 109533, 2022. [arXiv]

Physics guided neural networks for modelling of non-linear dynamics

Robinson, H., Pawar, S., Rasheed, A., and San, O.  Neural Networks, 154, 333-345, 2022. [arXiv]

Multi-fidelity information fusion with concatenated neural networks

Pawar, S., San, O., Vedula, P., Rasheed, A. and Kvamsdal, T. Scientific Reports, 12, 5900, 2022. [arXiv]

On closures for reduced order models – A spectrum of first-principle to machine-learned avenues 

Ahmed, S. E., Pawar, S., San, O., Rasheed, A., Iliescu, T. and Noack, B. R.  Physics of Fluids, 33, 091301, 2021.  [arXiv]

Hybrid analysis and modeling, eclecticism, and multifidelity computing toward digital twin revolution

San, O., Rasheed, A. and Kvamsdal, T. GAMM Mitteilungen (Surveys for Applied Mathematics and Mechanics), 44, e202100007, 2021. [arXiv]

Data assimilation empowered neural network parameterizations for subgrid processes in geophysical flows 

Pawar, S. and San, O. Physical Review Fluids, 6, 050501, 2021. [arXiv]

Digital twin: values, challenges and enablers from a modeling perspective

Rasheed, A., San, O. and Kvamsdal, T.  IEEE Access, 8, 21980-22012, 2020.   [arXiv]

Sub-grid scale model classification and blending through deep learning

Maulik, R., San, O., Jacob, J. and Crick, C. Journal of Fluid Mechanics, 870, 784-812, 2019. [arXiv]

Sub-grid modelling for two-dimensional turbulence using neural networks 

Maulik, R., San, O., Rasheed, A. and Vedula, P. Journal of Fluid Mechanics, 858, 122-144, 2019.  [arXiv]

A neural network approach for the blind deconvolution of turbulent flows

Maulik, R. and San, O. Journal of Fluid Mechanics, 831, 151-181, 2017. [arXiv]


Journal Articles






Highlighted by Savannah Mandel, Scilight, American Institute of Physics: Non-intrusive approach to reducedorder modeling of fluid flows.





Book Chapters

Edited Books

Conference Papers

Invited Seminars & Lectures

Invited Talks & Conference Presentations (with Abstracts)