Omer San

5.3k citations

125 papers · 3.4k indexed · 1 hit paper · h-index 30

Impact in

Statistical and Nonlinear Physics top 0.5%
- Model Reduction and Neural Networks
Industrial and Manufacturing Engineering top 0.5%
- Digital Transformation in Industry
- Flexible and Reconfigurable Manufacturing Systems

Papers in

Statistical and Nonlinear Physics 52
- Model Reduction and Neural Networks 50
Computational Mechanics 77
- Fluid Dynamics and Turbulent Flows 49
- Fluid Dynamics and Vibration Analysis 24
- Computational Fluid Dynamics and Aerodynamics 24
- Lattice Boltzmann Simulation Studies 7

Co-authors: Adil Rasheed Trond Kvamsdal Romit Maulik Suraj Pawar Traian Iliescu Anne Staples Sk. Mashfiqur Rahman Kursat Kara
Journals: Physics of Fluids (8 papers)Computers & Fluids (5 papers)International journal of computational fluid dynamics (5 papers)Scientific Reports (4 papers)IEEE Access (4 papers)
Partner nations: United States Norway Romania

In The Last Decade

Omer San

120 papers receiving 3.3k citations

Hit Papers

align trajectories log scale

Digital Twin: Values, Challenges and Enablers From a Modeling Perspective 2020 · 1.0k citations

Peers

Countries citing papers authored by Omer San

Since Specialization

Citations

This map shows the geographic impact of Omer San's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Omer San with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Omer San more than expected).

Fields of papers citing papers by Omer San

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Omer San. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Omer San. The network helps show where Omer San may publish in the future.

Co-authors

The 25 scholars most cited alongside Omer San, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Omer San Line = papers co-authored together Omer San links everyone, so they are left out of the graph.

All Works

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20 of 20 papers shown

#	Work
1	A gray-box model for unitary air conditioners developed with symbolic regression International Journal of Refrigeration ·K. Zahradník, Craig R. Bradshaw, Rushikesh Kamalapurkar, Omer San	2024	11
2	Digital Twin for Wind Energy: Latest Updates From the NorthWind Project Adil Rasheed, J.A. Walburg, 晃宏五十嵐, Mandar Tabib, P.M. NESLING, 정옥, Kjetil Johannessen, Trond Kvamsdal, Omer San, John Olav Tande, Idar Barstad, 藤田弘美, Herman L. Gilster, X Zhang, Hung‐Leo Lin	2024	1
3	Enhancing elasticity models with deep learning: A novel corrective source term approach for accurate predictions Applied Soft Computing ·Amouzouvi Gagnon, Manoj Shrestha -, Adil Rasheed, Trond Kvamsdal, Omer San	2024	2
4	Enhancing wind field resolution in complex terrain through a knowledge-driven machine learning approach Engineering Applications of Artificial Intelligence ·Title. R. C. Morris., J.A. Walburg, Adil Rasheed, Mandar Tabib, Omer San, Kristina Kullgraf	2024	2
5	Blending machine learning and sequential data assimilation over latent spaces for surrogate modeling of Boussinesq systems Physica D Nonlinear Phenomena ·鉄夫小池, Цвиёвич Александр Милиславович, Omer San	2023	3
6	Artificial Intelligence-Driven Digital Twin of a Modern House Demonstrated in Virtual Reality IEEE Access ·Moreano Merchán, Adil Rasheed, Omer San	2023	17
7	Physics guided neural networks for modelling of non-linear dynamics Neural Networks ·A. C. Горшков, Suraj Pawar, Adil Rasheed, Omer San	2022	46
8	Equation‐Free Surrogate Modeling of Geophysical Flows at the Intersection of Machine Learning and Data Assimilation Journal of Advances in Modeling Earth Systems ·Suraj Pawar, Omer San	2022	14
9	Combining physics-based and data-driven techniques for reliable hybrid analysis and modeling using the corrective source term approach Applied Soft Computing ·Manoj Shrestha -, Adil Rasheed, Trond Kvamsdal, Omer San	2022	32
10	Prospects of federated machine learning in fluid dynamics AIP Advances ·Omer San, Suraj Pawar, Adil Rasheed	2022	5
11	Data assimilation empowered neural network parametrizations for subgrid processes in geophysical flows Physical Review Fluids ·Suraj Pawar, Omer San	2021	24
12	Hybrid analysis and modeling, eclecticism, and multifidelity computing toward digital twin revolution GAMM-Mitteilungen ·Omer San, Adil Rasheed, Trond Kvamsdal	2021	43
13	Feature engineering and symbolic regression methods for detecting hidden physics from sparse sensor observation data Duo Research Archive (University of Oslo) ·Gaofan Fan, Adil Rasheed, Anne Staples, Omer San	2020	54
14	Taming an Autonomous Surface Vehicle for Path Following and Collision Avoidance Using Deep Reinforcement Learning IEEE Access ·Shin-Hwa Suh, A. C. Горшков, Adil Rasheed, Omer San	2020	57
15	Digital Twin: Values, Challenges and Enablers From a Modeling Perspective IEEE Access ·Adil Rasheed, Omer San, Trond Kvamsdal Hit paper breakdown →	2020	1025
16	COLREG-Compliant Collision Avoidance for Unmanned Surface Vehicle Using Deep Reinforcement Learning IEEE Access ·Shin-Hwa Suh, J. Handsaeme, Adil Rasheed, Omer San	2020	64
17	Long short-term memory embedded nudging schemes for nonlinear data assimilation of geophysical flows Physics of Fluids ·Suraj Pawar, Shady E. Ahmed, Omer San, Adil Rasheed, I. M. Navon	2020	38
18	Forward sensitivity approach for estimating eddy viscosity closures in nonlinear model reduction Physical review. E ·Shady E. Ahmed, N. Aidargaliyeva, Omer San, Adil Rasheed	2020	2
19	Memory embedded non-intrusive reduced order modeling of non-ergodic flows Physics of Fluids ·Shady E. Ahmed, Sk. Mashfiqur Rahman, Omer San, Adil Rasheed, I. M. Navon	2019	37
20	Deep learning based sub-grid scale closure for LES of Kraichnan turbulence Bulletin of the American Physical Society ·Suraj Pawar, Omer San, Adil Rasheed	2019	1

About Omer San

Omer San is a scholar working on Statistical and Nonlinear Physics, Computational Mechanics, Statistics, Probability and Uncertainty, Atmospheric Science and Aerospace Engineering, having authored 125 papers that have together received 3.4k indexed citations. Recurring topics across this work include Model Reduction and Neural Networks (50 papers), Fluid Dynamics and Turbulent Flows (49 papers), Meteorological Phenomena and Simulations (29 papers), Fluid Dynamics and Vibration Analysis (24 papers), Computational Fluid Dynamics and Aerodynamics (24 papers), Probabilistic and Robust Engineering Design (12 papers), Wind and Air Flow Studies (9 papers) and Lattice Boltzmann Simulation Studies (7 papers). The work is most often cited by research in Statistical and Nonlinear Physics (1.1k citations), Industrial and Manufacturing Engineering (650 citations), Computational Mechanics (1.2k citations), Statistics, Probability and Uncertainty (267 citations) and Atmospheric Science (333 citations). Omer San has collaborated with scholars based in United States, Norway and Romania. Frequent co-authors include Adil Rasheed, Trond Kvamsdal, Romit Maulik, Suraj Pawar, Traian Iliescu, Anne Staples, Sk. Mashfiqur Rahman, Kursat Kara, Shady E. Ahmed and Prakash Vedula. Their work appears in journals such as Physics of Fluids, Computers & Fluids, International journal of computational fluid dynamics, Scientific Reports and IEEE Access.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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