Journal Articles

2021

1. Gupta, P., Taskar B., Steen, S., Rasheed, A. Statistical modeling of Ship’s hydrodynamic performance indicator, Applied Ocean Research, 111(102623), 2021
2. Sundby, T.; Graham, J.M.; Rasheed, A.; Tabib, M.; San, O. Geometric Change Detection in Digital Twins. Digital 2021, 1, 111-129.
3. Ahmed, S. E., Pawar, S., San, O., Rasheed, A. and Tabib, M. A nudged hybrid analysis and modeling approach for realtime wake-vortex transport and decay prediction. Computers and Fluids, 221, 104895, 2021.
4. Stavelin, H., Rasheed, A., San, O. and Hestnes, A. J. Applying object detection to marine data and exploring explainability of a fully convolutional neural network using principal component analysis. Ecological Informatics 62, 101269, 2021.
5. Ahmed, S. E., San, O., Kara, K., Younis, R. and Rasheed, A. Multifidelity computing for coupling full and reduced order models. PLOS ONE, 16(2), e0246092, 2021.
6. Havenstrøm, S. T., Rasheed, A. and San, O. Deep reinforcement learning controller for 3D path following and collision avoidance by autonomous underwater vehicles. Frontiers in Robotics and AI, 7, 566037, 2021.
7. Pawar, S., San, O., Aksoylu, B., Rasheed, A. and Kvamsdal, T. Physics guided machine learning using simplified theories. Physics of Fluids, 33, 011701, 2021.
8. Pawar, S. and San, O. Data assimilation empowered neural network parameterizations for subgrid processes in geophysical flows. Physical Review Fluids, accepted, 2020.
9. Mou, C., Koc, B., San, O. , Rebholz, L. G. and Iliescu, T. Data-driven variational multiscale reduced order models. Computer Methods in Applied Mechanics and Engineering, 373, 113470, 2021.

2020

1. Ahmed, S. E., Pawar, S. and San, O. PyDA: A hands-on introduction to dynamical data assimilation with Python. Fluids, 5(4), 225, 2020.
2. Ahmed, S. E., San, O., Kara, K., Younis, R. and Rasheed, A. Interface learning of multiphysics and multiscale systems. Physical Review E, 102, 053304, 2020.
3. Ahmed, S. E., Bhar, K., San, O. and Rasheed, A. Forward sensitivity approach for estimating eddy viscosity closures in nonlinear model reduction. Physical Review E, 102, 043302, 2020.
4. Pawar, S., Ahmed, S. E. and San, O. Interface learning in fluid dynamics: statistical inference of closures within micro-macro coupling models. Physics of Fluids, 32, 091704, 2020. (Featured Article)
5. Eivind Meyer, Amalie Heiberg, Rasheed, Adil, and Omer San. COLREG-compliant collision avoidance for unmanned surface vehicle using deep reinforcement learning. IEEE Access, 8:165344– 165364, 2020
6. Simen Theie Havenstrøm, Camilla Sterud, Adil Rasheed, and Omer San. Proportional integral derivative controller assisted reinforcement learning for path following by autonomous underwater vehicles. arXiv e-prints, page arXiv:2002.01022, Feb 2020
7. Suraj Pawar, Shady E. Ahmed, Omer San, Adil Rasheed, and Ionel M. Navon. Long short-term memory embedded nudging schemes for nonlinear data assimilation of geophysical flows. Physics of Fluids, 32:076606, 2020
8. Suraj Pawar, Shady E. Ahmed, O. San, and A. Rasheed. An evolve-then-correct reduced order model for hidden fluid dynamics. Mathematics, 8(4):570, 2020
9. Eivind Meyer, Haakon Robinson, Rasheed, Adil, and Omer San. Taming an autonomous surface vehicle for path following and collision avoidance using deep reinforcement learning. IEEE Access, 8:41466–41481, 2020
10. Shady E Ahmed, Omer San, Rasheed, Adil, and Traian Iliescu. A long short term memory for hybrid uplifted reduced order models. Physica D: Nonlinear Phenomena, 409:132471, 2020
11. Suraj Pawar, Omer San, Rasheed, Adil, and Prakash Vedula. A priori analysis on deep learning of subgrid-scale parameterizations for kraichnan turbulence. Theoretical and Computational Fluid Dynamics, 34:429–455, 2020
12. Suraj Pawar, Shady E Ahmed, Omer San, and Rasheed, Adil. Data-driven recovery of hidden physics in reduced order modeling of fluid flows. Physics of Fluids, 32:036602, 2020
13. Rasheed, Adil, Omer San, and Trond Kvamsdal. Digital Twin: Values, Challenges and Enablers from a modeling perspective. IEEE Access, 8:21980–22012, 2020
14. Harsha Vaddireddy, Rasheed, Adil, Anne E Staples, and Omer San. Feature engineering and symbolic regression methods for detecting hidden physics from sparse sensors. Physics of Fluids, Editor’s pick, 32:015113, 2020
15. Muhammad Salman Siddiqui, Rasheed, Adil, and Trond Kvamsdal. Numerical assessment of rans turbulence models for the development of data driven reduced order models. Ocean Engi- neering, 196:106799, 2020
16. Ahmed, S. E., Pawar, S. and San, O. PyDA: A hands-on introduction to dynamical data assimilation with Python. Fluids, 5(4), 225, 2020.
17. Pawar, S., Ahmed, S. E. and San, O. Interface learning in fluid dynamics: statistical inference of closures within micro-macro coupling models. Physics of Fluids, 32, 091704, 2020.
18. Ahmed, M., Park, H., Bach, C. K. and San, O. Numerical Investigation of Air Mixer for HVAC Testing Applications (ASHRAE RP-1733). Science and Technology for the Built Environment, 26(9), 1252-1273, 2020.
19. Maulik, R. and San, O. Numerical assessments of a parametric implicit large eddy simulation model. Journal of Computational and Applied Mathematics, 376, 112866, 2020.
20. Maulik, R., San, O. and Jacob, J. D. Spatiotemporally dynamic implicit large eddy simulation using machine learning classifiers. Physica D: Nonlinear Phenomena, 406, 132409, 2020.
21. Ahmed, S. E. and San, O. Breaking the Kolmogorov barrier in model reduction of fluid flows. Fluids, 5, 26, 2020.
22. Ahmed, S. E., San, O., Bistrian, D. A. and Navon, I. M. Sampling and resolution characteristics in reduced order models of shallow water equations: intrusive vs non‐intrusive. International Journal for Numerical Methods in Fluids, 92 (8), 992-1036, 2020.

2019

1. Shady E Ahmed, Sk Mashfiqur Rahman, Omer San, Rasheed, Adil, and Ionel M Navon. Memory embedded non-intrusive reduced order modeling of non-ergodic flows. Physics of Fluids, 31:126602, 2019
2. Sk Mashfiqur Rahman, Suraj Pawar, Omer San, Rasheed, Adil, and Traian Iliescu. A non- intrusive reduced order modeling framework for quasi-geostrophic turbulence. Physical Review E, 100:053306, 2019
3. Muhammad Salman Siddiqui, Rasheed, Adil, and Trond Kvamsdal. Validation of the numerical simulations of flow around a scaled-down turbine using experimental data from wind tunnel. Wind and Structures, 29:405–416, 2019
4. Suraj Pawar, Sk Mashfiqur Rahman, Harsha Vaddireddy, Omer San, Rasheed, Adil, and Prakash Vedula. A deep learning enabler for non-intrusive reduced order modeling of fluid flows. Physics of Fluids, Editor’s Pick, 31:085101, 2019
5. Muhammad Salman Siddiqui, Eivind Fonn, Trond Kvamsdal, and Rasheed, Adil. Finite-volume high-fidelity simulation combined with finite-element-based reduced-order modeling of incompress- ible flow problems. Energies, 12:1271, 2019
6. Eivind Fonn, Harald van Brummelen, Trond Kvamsdal, and Rasheed, Adil. Fast divergence- conforming reduced basis methods for steady navier–stokes flow. Computer Methods in Applied Mechanics and Engineering, 346:486–512, 2019
7. Romit Maulik, Omer San, Rasheed, Adil, and Prakash Vedula. Sub-grid modelling for two- dimensional turbulence using neural networks. Journal of Fluid Mechanics, 858:122–144, 2019
8. Romit Maulik, Omer San, Rasheed, Adil, and Prakash Vedula. Data-driven deconvolution for large eddy simulations of kraichnan turbulence. Physics of Fluids, 30:125109, 2018
9. Sk. Mashfiqur Rahman, Omer San, and Rasheed, Adil. A hybrid approach for model order reduction of barotropic quasi-geostrophic turbulence. Fluids, 3(4):125109, 2018
10. Mandar V. Tabib, Ole Martin Løvvik, Kjetil Johannessen, Rasheed, Adil, Espen Sagvolden, and Anne Marthine Rustad. Discovering thermoelectric materials using machine learning: Insights and challenges. In Věra Kůrková, Yannis Manolopoulos, Barbara Hammer, Lazaros Iliadis, and Ilias Maglogiannis, editors, Artificial Neural Networks and Machine Learning – ICANN 2018, pages 392–401, Cham, 2018. Springer International Publishing
11. Muhammad Salman Siddiqui, Rasheed, Adil, Mandar V Tabib, and Trond Kvamsdal. Numerical investigation of modeling frameworks and geometric approximations on nrel 5 mw wind turbine. Renewable Energy, 132:1058–1075, 2019
12. Pawar, S. and San, O. CFD Julia: A learning module structuring an introductory course on computational fluid dynamics. Fluids, 4(3), 159, 2019.
13. Vaddireddy, H. and San, O. Equation discovery using fast function extraction: a deterministic symbolic regression approach. Fluids, 4(2), 111, 2019.
14. Rahman, S. M., Ahmed, S. and San, O. A dynamic closure modeling framework for model order reduction of geophysical flows. Physics of Fluids, 31, 046602, 2019.
15. 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.
16. San, O., Maulik, R. and Ahmed, M. An artificial neural network framework for reduced order modeling of transient flows. Communications in Nonlinear Science and Numerical Simulation, 77, 271-287, 2019.
17. Rahman, S. M. and San, O. A relaxation filtering approach for two-dimensional Rayleigh–Taylor instability-induced flows. Fluids, 4(2), 78, 2019.
18. Rahman, S. M. and San, O. A localized dynamic closure model for Euler turbulence. International Journal of Computational Fluid Dynamics, 32(8-9), 326-378, 2019.
19. Sk Mashfiqur Rahman, Rasheed, Adil, and Omer San. A hybrid analytic framework for accel- erating incompressible flow solvers. Fluids, 3(3):50, 2018
20. Knut Nordanger, Rasheed, Adil, Knut Morten Okstad, Arne Morten Kvarving, Runar Holdahl, and Trond Kvamsdal. Numerical benchmarking of fluid-structure interaction: An isogeometric finite element approach. Ocean Engineering, 124:324–339, 2016
21. Knut Nordanger, Runar Holdahl, Arne Morten Kvarving, Trond Kvamsdal, and Rasheed, Adil. Simulation of airflow past a 2d naca0015 airfoil using an isogeometric incompressible navier-stokes solver with the spalart-allmaras turbulence model. Computer Methods in Applied Mechanics and Engineering, 290:183–208, 2015
22. Knut Nordanger, Runar Holdahl, Arne Morten Kvarving, Rasheed, Adil, and Trond Kvamsdal. Implementation and comparison of three isogeometric navier–stokes solvers applied to simulation of flow past a fixed 2d naca0012 airfoil at high reynolds number. Computer Methods in Applied Mechanics and Engineering, 284:664–688, 2014
23. Rasheed, Adil and Asif Mushtaq. Numerical analysis of the flying condition at the alta airport, norway. Aviation, 18:109–119, 2014
24. Rasheed, Adil and Karstein Sørli. Cfd analysis of terrain induced turbulence at kristiansand airport, kjevik. Aviation, 17:104–112, 2013
25. Rasheed, Adil and Darren Robinson. Characterization of dispersive fluxes in mesoscale models using les of flow over an array of cubes. International Journal of Atmospheric Sciences, 17:898095, 2013
26. Rasheed, Adil, Darren Robinson, Alain Clappier, Chidambaram Narayanan, and Djamel Lake- hal. Representing complexities in urban geometry in mesoscale modeling. International Journal of Climatology, 31:289–301, 2011